Shield Ai Valuation Multiple Jobs in Usa

Senior Business Analyst Life & Annuities

Onsite in WDM office 4 days a week

Contract

Overview:

We are seeking an experienced Senior Business Systems Analyst (AI Enablement) to support ***’s AI transformation initiatives. This role will partner with ***’s BSA team, IT leadership, and PMO to understand current SDLC processes, identify pain points, gaps, and inconsistencies across teams, and translate findings into a more streamlined, AI-enabled operating model.

The BSA will assess our current requirements elicitation process and help define how an AI-infused approach can significantly improve the thoroughness, completeness, and quality of user requirements specifically for consumption by an AI coding agent. This includes identifying cross-system dependencies, non-functional requirements, and user personas from initial requirement elicitation through Jira story creation. This role will help transform our BSA capabilities by leveraging custom-built AI solutions to accelerate cycle time, improve consistency, and reduce downstream rework.

This role will also define measurable success criteria and develop metrics to evaluate process improvements and AI impact across the SDLC: gathering requirements across several other SDLC-related AI initiatives and provide light project management support across multiple AI workstreams.

Key Responsibilities

Assess current BA workflows, documentation standards, and impact analysis processes from user requirement elicitation through Jira story creation, identifying gaps, redundancies, inconsistencies and improvements across teams.

Identify friction points and define AI-enabled use cases with measurable business outcomes.

Drive feedback loops for AI proofs of concept (POCs) delivered to teams by gathering structured user input, measuring adoption and effectiveness, and incorporating insights into iterative improvements.

Develop clear, actionable requirements including user stories, acceptance criteria, non-functional requirements, personas, and upstream/downstream system impacts.

Determine and help establish processes or patterns to ensure user requirements align with current system capabilities. Proactively identify cross-system dependencies, technical constraints, gaps, and system limitations in collaboration with engineering teams to ensure requirements are technically feasible and implementation-ready.

Support backlog grooming, sprint planning, demos, and pilot validation sessions.

Track progress across multiple AI initiatives, manage dependencies and risks, and maintain clear stakeholder communication.

Develop and maintain lightweight project plans, timelines, and status reporting to ensure AI initiatives remain aligned with business priorities and strategic objectives.

Required Qualifications

5+ years of Business Analysis or Business Systems Analysis experience in technology or software environments.

Strong requirements elicitation, documentation, and facilitation skills.

Experience working in Agile environments with cross-functional teams.

Practical familiarity with AI tools (e.g., Claude Code CLI, ChatGPT, Copilot) and understanding of AI concepts (LLMs, prompt engineering, AI governance, and AI risk considerations).

Strong systems thinking, structured communication, and stakeholder management skills.

Preferred Experience

Experience implementing AI or automation solutions within enterprise environments.

Familiarity with AWS AI services (e.g., AWS Bedrock) or other enterprise AI platforms.

Familiarity with Jira, Confluence, GitHub, or similar SDLC ecosystems.

Experience supporting PMO processes or managing multiple concurrent initiatives along with a functional understanding of Agile practices.

Financial services or insurance industry experience.

Success Measures

Successful delivery of an AI-enabled solution for ***’s BSA team.

Improved BA workflow efficiency, quality, and consistency.

High-quality, implementation-ready requirements that reduce downstream rework and delivery thrash.

Effective coordination, transparency, and measurable progress across AI initiatives.

Join the team leading the next evolution of virtual care.

At Teladoc Health, you are empowered to bring your true self to work while helping millions of people live their healthiest lives.

Here you will be part of a high-performance culture where colleagues embrace challenges, drive transformative solutions, and create opportunities for growth. Together, we're transforming how better health happens.

Summary of Position

The Principal AI Security Engineer is a senior technical leader on the AI Security team, responsible for designing, building, and operating security controls for generative AI and Machine Learning (ML) systems across their full lifecycle: data, training, deployment, and runtime.

This role is deeply hands-on: you will work directly with data science, MLOps, platform, devops and application teams to secure LLMs, RAG systems, AI agents, and AI-enabled products. You will also lead the intake and review process for AI use cases, helping the organization adopt AI safely and at scale in a highly regulated environment.

The ideal candidate combines:

* Strong security engineering and cloud architecture experience

* Deep, current familiarity with modern AI/LLM tooling and practices

* Familiar and can cover basic coding within the AI tooling space (python, others)

* The ability to communicate clearly with senior leadership and influence enterprise-wide strategy

Essential Duties and Responsibilities

Secure AI / ML platforms and workloads

* Lead security architecture and threat modeling for AI/ML systems, including LLMs, RAG pipelines, agents, and AI-powered applications.

* Design and implement security controls as code (services, libraries, infrastructure-as-code, policy-as-code) for AI/ML platforms and workloads.

* Lead and help setup the basic infrastructure needed to safely rollout AI - MCPs, LLMs, pipelines, Test harness for AI (ie: harmbench), intake automation.

* Partner with data science and MLOps teams to harden:

• Data ingestion and labeling
• Training and fine-tuning pipelines
• Model registries and deployment workflows
• Inference APIs, agents, and integrations

* Define and champion secure reference architectures and patterns for common AI use cases and focus on composable architecture.

AI use case intake & governance

* Design, implement, and continuously improve the intake, triage, and review process for AI/ML and generative AI use cases across the organization.

* Build and automate self-service workflows (e.g., request forms, risk questionnaires, routing, approvals) that balance speed of delivery with security, privacy, and compliance with a focus on risk scoring and scorecards.

* Define risk-based criteria for AI use case approval, including data sensitivity, model and vendor selection, integration patterns, and control requirements; this will involve in re-mapping the complete end to end lifecycle.

* Review proposed AI solutions from concept through deployment, providing clear, actionable guidance to product and engineering teams.

* Maintain visibility into the AI use case portfolio and risk posture, and provide regular reporting to leadership and governance bodies.

Monitoring, detection & assurance

* Establish and maintain monitoring and detection for AI-specific threats, such as:

• Prompt injection and jailbreak attempts
• Data exfiltration and sensitive data exposure
• Misuse or abuse of AI tools and agents
• Anomalous model or pipeline behavior

* Integrate AI/ML systems with existing logging, SIEM, and incident response processes.

* Lead or participate in AI-focused security assessments, red-teaming, and adversarial testing; drive remediation and verification.

Strategy, leadership & enablement

* Help define and evolve the organization's AI security strategy, standards, and roadmap in partnership with Security, Engineering, Data, Legal, Privacy, and Risk.

* Translate global privacy, data sovereignty, and regulatory requirements into practical technical controls for AI workloads across multiple cloud environments.

* Prepare and deliver executive-ready briefings and narratives on AI security risks, controls, and progress.

* Mentor other engineers and serve as THE internal subject matter expert on AI/ML security, generative AI, and LLM-based systems.

Qualifications Expected for Position

• 7+ years of experience in information security, security engineering, or related fields, including significant time building and securing production systems.
• 3+ years of hands-on experience with AI/ML technologies (such as LLMs, RAG, model training/fine-tuning, MLOps, or AI-powered products), including implementation of security controls or guardrails for these systems.
• Strong programming skills in one or more relevant languages (e.g., Python, TypeScript/JavaScript, Go, or similar), with a track record of contributing to production-grade tools, services, or libraries.
• Deep understanding of cloud security architecture and controls on at least one major cloud platform (AWS, Azure, or GCP), including identity, networking, secrets management, data protection, logging, and monitoring.
• Experience designing and implementing controls in a highly regulated environment; healthcare or financial services preferred.
• Demonstrated ability to lead complex technical initiatives across multiple teams, from problem definition through design, implementation, and adoption.
• Proven ability to communicate complex technical and risk topics clearly to both engineering teams and senior leadership.

Preferred Qualifications:

* Practical experience securing LLM- and genAI-based systems, such as:

• RAG architectures backed by internal data
• AI assistants, copilots, or agents integrated with enterprise tools
• Fine-tuned models and model hosting platforms

* Experience with AI IDE tools

• cursor, windsurfer, others
• Knows the security problems and has practical solutions that balances innovation with innovation.

* Familiarity with AI/ML frameworks and ecosystems (e.g., TensorFlow, PyTorch, Scikit-learn) and/or modern LLM development stacks and IDEs (e.g., API-based LLMs, self-hosted models, AI-enhanced coding tools).

* Experience with:

• Security for data pipelines, feature stores, and model registries
• Detection engineering or SIEM tuning for AI-related events
• Red-teaming or adversarial testing of AI systems

* Evidence of ongoing engagement with AI and security (such as side projects, open-source contributions, lab environments, publications, or conference talks).

* Familiarity with emerging AI security and safety standards and forward-looking industry guidance and horizon reports.

* Relevant certifications (e.g., cloud security, security engineering, or governance) are a plus.

* Strong analytical and problem-solving skills, with the ability to operate effectively in a fast-evolving technical and regulatory landscape.

* High level of integrity and ethical conduct.

This role is a fit if you:

* Regularly build, break, or secure AI/ML or LLM-based systems in your day-to-day work or personal projects.

* Are comfortable reading and writing code, experimenting with new AI tools, and wiring them into real systems.

* Enjoy turning ambiguous AI ideas and risks into concrete architectures, controls, and automation.

* Can move fluidly between deep technical discussions and concise, executive-level explanations.

This role is not a fit if you:

* Prefer to focus solely on policy, governance, or vendor assessments without hands-on technical work.

* Do not actively engage with current AI/LLM tooling, research, and emerging practices.

* "Describe a specific LLM or AI/ML system you have secured. What were the main risks and what controls did you implement?"

* "What AI tools, libraries, or environments do you actively use or experiment with today (work or personal), and for what?"

* "What do you see as the most important AI security or safety developments on the horizon over the next few years, and why?"

The base salary range for this position is$180,000 - $190,000. In addition to a base salary, this position is eligible for a performance bonus and benefits (subject to eligibility requirements) listed here: Teladoc Health Benefits 2026.Total compensation is based on several factors including, but not limited to, type of position, location, education level, work experience, and certifications.This information is applicable for all full-time positions.

We follow a Flexible Vacation Policy, intended for rest, relaxation, and personal time. All time off must be approved by your manager prior to use. You will also receive 80 hours of Paid Sick, Safe, and Caregiver Leave annually. This applies to full-time positions only. If you are applying for a part-time role, your recruiter can provide additional details.

As part of our hiring process, we verify identity and credentials, conduct interviews (live or video), and screen for fraud or misrepresentation. Applicants who falsify information will be disqualified.

Teladoc Health will not sponsor or transfer employment work visas for this position. Applicants must be currently authorized to work in the United States without the need for visa sponsorship now or in the future.

Why join Teladoc Health?

• Teladoc Health is transforming how better health happens. Learn how when you join us in pursuit of our impactful mission.

• Chart your career path with meaningful opportunities that empower you to grow, lead, and make a difference.

• Join a multi-faceted community that celebrates each colleague's unique perspective and is focused on continually improving, each and every day.

• Contribute to an innovative culture where fresh ideas are valued as we increase access to care in new ways.

• Enjoy an inclusive benefits program centered around you and your family, with tailored programs that address your unique needs.

• Explore candidate resources with tips and tricks from Teladoc Health recruiters and learn more about our company culture by exploring #TeamTeladocHealth on LinkedIn.

As an Equal Opportunity Employer, we never have and never will discriminate against any job candidate or employee due to age, race, religion, color, ethnicity, national origin, gender, gender identity/expression, sexual orientation, membership in an employee organization, medical condition, family history, genetic information, veteran status, marital status, parental status, or pregnancy). In our innovative and inclusive workplace, we prohibit discrimination and harassment of any kind.

Teladoc Health respects your privacy and is committed to maintaining the confidentiality and security of your personal information. In furtherance of your employment relationship with Teladoc Health, we collect personal information responsibly and in accordance with applicable data privacy laws, including but not limited to, the California Consumer Privacy Act (CCPA). Personal information is defined as: Any information or set of information relating to you, including (a) all information that identifies you or could reasonably be used to identify you, and (b) all information that any applicable law treats as personal information. Teladoc Health's Notice of Privacy Practices for U.S. Employees' Personal information is available at this link.

Job Title: Sr AI Platform Engineer- AI Platform Engineer (Guardrails, Observability & Evaluation Infrastructure)

Location, Charlotte, NC, USA (3 days onsite)

Role Overview

AI Platform Engineer to design and build the foundational components that power enterprise scale GenAI applications. This includes data guardrails, model safety tooling, observability pipelines, evaluation harnesses, and standardized logging/monitoring frameworks. This role is critical for enabling safe, reliable, and compliant AI development across multiple use cases, teams, and business units. Idea is to create the common platform services that AI team will build upon.

Key Responsibilities

1. Guardrails, Safety & Governance

• Design and implement data guardrail frameworks (pre processing, redaction, PII/PHI filtering, DLP integration, prompt defenses).

• Build "Model Armor" components such as:

o Input validation & sanitization

o Prompt injection defenses

o Harmful content detection & policy enforcement

o Output filtering, fact checking, grounding checks

• Integrate safety tooling (policy engines, classifiers, DLP APIs, safety models).

• Collaborate with Security, Compliance, and Data Privacy teams to ensure frameworks meet enterprise governance requirements.

2. Observability Frameworks

• Build and maintain observability pipelines using tools like Arize AI (tracing, quality metrics, dataset drift/hallucination tracking, embedding monitoring).

• Define and enforce platform wide standards for:

o Tracing LLM calls

o Token usage and cost monitoring

o Latency and reliability metrics

o Prompt/model version tracking

• Provide reusable SDKs or middleware for engineering teams to adopt observability with minimal friction.

3. Logging, Monitoring & Telemetry

• Design standardized LLM-specific logging schemas, including:

o Inputs/outputs

o Model metadata

o Retrieval metadata

o Safety flags

o User context and attribution

• Build monitoring dashboards for performance, cost, anomalies, errors, and safety events.

• Implement alerting and SLOs/SLIs for LLM inference systems.

4. Evaluation Infrastructure

• Architect and maintain evaluation harnesses for GenAI systems, including:

o RAG evaluation (faithfulness, relevance, hallucination risk)

o Summarization/QA evaluation

o Human-in-the-loop review workflows

o Automated eval pipelines integrated into CI/CD

• Support frameworks such as RAGAS, G Eval, rubric scoring, pairwise comparisons, and test case generation.

• Build reusable tooling for teams to write, run, and track model evaluations.

5. Platform Engineering & Reusable Components

• Develop shared libraries, APIs, and services for:

o Prompt management/versioning

o Embedding pipelines and model wrappers

o Retrieval adapters

o Common data loaders and document preprocessing

o Tool/function schemas

• Drive consistency across teams with standards, reference architectures, and best practices.

• Review system designs across use cases to ensure alignment to platform patterns.

6. Collaboration & Enablement

• Partner with AI engineers, product teams, and data scientists to understand cross cutting needs and convert them into reusable platform features.

• Create documentation, onboarding guides, examples, and developer tooling.

• Provide internal training (brown bags, workshops) on guardrails, observability, and evaluation frameworks.

Required Qualifications

Technical Skills

• 5–10+ years software engineering or ML infrastructure experience.

• Strong Python engineering fundamentals (FastAPI, async, typing/Pydantic, testing).

• Experience with model safety/guardrails approaches (prompt injection defense, PII redaction, toxicity filters, policy enforcement).

• Hands on with Arize AI, LangSmith, or similar LLM observability platforms.

• Experience creating evaluation frameworks using RAGAS, G Eval, or custom rubric systems.

• Strong familiarity with vector databases (Pinecone, Weaviate, Milvus), embeddings, and retrieval pipelines.

• Solid understanding of LLM architectures, tokenization, embeddings, context limits, and RAG patterns.

• Experience in cloud (GCP preferred), Kubernetes/GKE, containers, and CI/CD.

• Strong understanding of security, governance, DLP, data privacy, RBAC, and enterprise compliance requirements.

Soft Skills

• Strong documentation and communication skills.

• Ability to influence engineering teams and standardize best practices.

• Comfortable working across multiple stakeholders—platform, security, ML engineering, product.

Nice to Have

• Experience with LangChain/LangGraph or LlamaIndex orchestrations.

• Experience with , Rebuff, Protect AI, or similar LLM security tooling.

• Experience with GCP Vertex AI pipelines, Model Monitoring, and Vector Search.

• Familiarity with knowledge graphs, grounding models, fact checking models.

• Building SDKs or developer frameworks adopted across multiple teams.

• On prem or hybrid AI deployment experience.

Onsite AI Engineer - Construction Industry Focus

New Haven, CT - Onsite 5 days per week

• Initial Assignment: Fully onsite 5 days per week at a construction site in Ft. Myers (FL) or New Haven (CT) for 1 year
• Post-Assignment: Relocation to one of the corporate offices for hybrid employment: Boston, MA (preferred), New York City (NY), New Haven (CT), Herndon (VA), West Palm Beach (FL), or Estero (FL)

Role Summary

As the on-site catalyst who turns AI ideas into working reality. Partnering with each project’s AI Champion (Project Manager or Superintendent), you’ll uncover pain points, redesign workflows, and deploy AI agents that cut down reporting, accelerate RFIs, simplify lookahead planning, progress updates, materials tracking, and more. When needed, you will develop user stories and coordinate development with the central AI Studio. You’ll help advance the vision of the “Construction Site of the Future,” showing how agentic AI will transform project operations.

Responsibilities

• Workflow discovery and redesign: Lead Lean/Six Sigma workshops; map value streams; log high-impact AI agent opportunities that improve field efficiency.
• AI agent development: Build and deploy multiple production-ready AI agents using Copilot Studio, Power Apps/Automate, ChatGPT Enterprise, or code-first frameworks. Integrate agents into Teams/SharePoint on the front end and Databricks Lakehouse or other enterprise data sources on the back end.
• RAG pipelines and LLMOps: Design and operate retrieval-augmented generation (RAG) pipelines with Databricks Delta Tables, Unity Catalog, and Vector Search (or Spark/Hadoop equivalents). Monitor cost, latency, adoption, and model drift.
• Cross-cloud orchestration: Blend OpenAI, Azure OpenAI, and AWS Bedrock services through secure custom connectors to maximize flexibility and adoption.
• Data integration: Partner with Data Engineering to deliver ETL/ELT pipelines, API integrations, and event-driven connectors that feed RAG pipelines and AI agents.
• Change management and adoption: Train field teams, gather feedback, iterate quickly, and embed agents into SOPs. Track usage and ROI with adoption metrics and behavior-change KPIs.
• Stakeholder communication: Translate technical results into business value for leadership and clients. Contribute use cases and playbooks for the “Construction Site of the Future.”
• Compliance and hand-offs: Ensure all AI solutions meet the company’s data governance and security standards. Draft clear user stories and specs for escalation to central AI/Data Engineering teams when necessary.

Qualifications

• 4+ years in AI engineering, data science, or ML-focused software engineering.
• Proven experience building multiple AI agents in production environments.
• 2+ years of hands-on experience with LLMs, RAG pipelines, and LLMOps practices.
• Must have strong traditional software engineering background in Python

Bonus Points

• Experience in construction, manufacturing, or other process-heavy industries.
• Advanced degree in a technical field.

Talent Acquisition Manager – AI Infrastructure & Engineering

San Francisco, California

Hybrid Working

We are expanding our global team and launching a new office in San Francisco.

WNTD is looking for an experienced Talent Acquisition Manager to join our Talent Solutions team and support the continued growth of AI infrastructure and accelerated compute platforms across North America.

This role will support one of the most significant AI infrastructure expansion programmes currently underway, focused on building next generation platforms powered by NVIDIA accelerated compute.

You will work closely with senior technical leaders to attract and hire talent across the full infrastructure stack including software engineering, AI platforms, GPU environments and large scale compute infrastructure.

This is a delivery focused role supporting high growth engineering programmes across AI infrastructure and cloud platforms.

The Role

You will lead hiring across multiple engineering disciplines spanning software engineering, AI infrastructure platforms and high performance compute environments.

Working closely with technical leadership and programme stakeholders, you will build pipelines of high quality candidates and manage fast moving hiring plans across several technical workstreams.

Key Responsibilities

• Build and manage talent pipelines across software engineering, AI infrastructure and GPU compute environments

• Proactively source talent across the United States through mapping, referrals and direct outreach

• Screen candidates for technical capability, experience and long term fit

• Partner with engineering leaders to define hiring priorities and role requirements

• Maintain clear tracking of hiring pipelines and delivery progress

• Support wider Talent Solutions activity during peak delivery phases

• Ensure a professional and consistent candidate experience

• Champion fair and inclusive hiring practices

Key Experience

• Proven experience hiring across complex engineering environments

• Strong track record building pipelines across software and infrastructure roles

• Comfortable engaging with technical stakeholders and discussing engineering topics

• Excellent communication and stakeholder management skills

• Strong organisation with the ability to manage multiple roles simultaneously

What We Offer

• Competitive salary and benefits

• Opportunity to support one of the fastest growing AI infrastructure build programmes globally

• Growth within a high performing delivery focused team

• Hybrid working model

• A collaborative culture that values ownership, pace and problem solving

Additional Requirements

• Ability to commute to the San Francisco office

• No visa sponsorship available

• Hybrid working model

Title: Lead Software Engineer - AI Application Platform

Mode of interview 1 round in person

Location: Must be in Charlotte, NC to work Hybrid Model

Main Skill set: Python, AI and Angular

Description:

Lead Software Engineer - AI Application Platform

The Opportunity

We are seeking a Lead Software Engineer to guide the architectural development and execution of the client, a sophisticated AI-powered application generation platform. This role suits a proven technical leader with deep, hands-on expertise across the full software stack who finds enabling a team to build better software deeply satisfying.

You will shape critical systems, mentor senior and junior developers through complex technical decisions, conduct rigorous code reviews across multiple technology domains, and directly influence the platform's trajectory through strategic engineering leadership.

This is for someone who:

• Engages thoughtfully when a junior developer asks targeted architectural questions—because you see an opportunity to shape how someone thinks about systems
• Takes time to explain subtle type-safety issues in code review, understanding that feedback is a teaching moment
• Can present architecture clearly to executives and confidently explain both what we're building and why it matters
• Finds more energy in the code your team ships than in the code you write individually
• Has proven depth across the full stack and a track record of developing engineers into stronger contributors

This is not a single-language codebase. The role requires the ability to make informed decisions on TypeScript design patterns, Python FastAPI architecture, AWS security posture, and Terraform state management in context with one another.

The Platform Challenge

The client is fundamentally a Platform-as-a-Service (PaaS) for dynamic application generation. This differs from building a traditional SaaS product. Rather than building one application, you're building infrastructure that enables users to build their own applications.

What this means architecturally:

• Dynamic Content Generation at Scale: Unlike traditional development where code is fixed, AppGen generates JSON form schemas, validation rules, and UI layouts on demand. The FormBuilder component doesn't know what fields will exist until runtime. The layout engine renders user-designed screens from configuration, not hardcoded templates.
• Multi-Tenant Isolation & Data Segregation: Each user gets their own generated app, potentially deployed to their own AWS environment. The architecture must account for data isolation, namespace management, and cross-tenant security considerations.
• User-Defined Data Structures: Traditional applications are built with predetermined database schemas. AppGen works differently—form structures, field types, and validation rules emerge from user conversations with Claude. This brings engineering challenges: How do you safely execute validation logic that users define? When users modify existing forms that have thousands of submissions, how do you maintain backward compatibility? How do you version schemas?
• Content Rendering, Not Code Generation: Unlike traditional no-code platforms where users drag-and-drop to build, AppGen uses AI instead. Users chat with Claude, Claude generates a form schema, and your platform renders that schema reliably across diverse field types, validation patterns, and workflows. The system renders configurations for immediate use, rather than generating code for later deployment.

Experience that directly transfers:

• You've contributed to or led development of low-code/no-code platforms (visual builders, workflow engines, configuration-driven systems)
• You've worked on SaaS platforms with multi-tenant architecture and understand isolation strategies, rate limiting, and per-customer customization
• You've built dynamic rendering systems that handle unknown/arbitrary schemas at runtime
• You've addressed the unique challenges of treating data configurations as user-created content (form builders, report designers, automation workflows)
• You understand the difference between platform infrastructure and applications built on that infrastructure—and the architectural implications of each

Core Responsibilities

1. Technical Architecture & Systems Thinking (40%)

• Shape architectural decisions across the full stack: How should the component layer handle dynamically generated forms? What's the right approach to validate complex cross-field dependencies in the FormBuilder? What separation of concerns makes sense between the Generator Lambda and the Parent Backend?
• Guide architecture discussions: Help senior developers think through design trade-offs. Should we use NgRx or Angular signals for this feature? When does a new Lambda function become worthwhile given cold-start costs?
• Identify and address system-wide bottlenecks: Work across layers to improve performance. Explore Lambda cold-start optimization, RDS query efficiency, and DynamoDB access patterns.
• Establish patterns and guide consistency: Define coding conventions that work across Python, TypeScript, and Terraform. Help new team members understand the reasoning behind architectural choices.
• What this looks like in practice: You're able to justify architectural decisions with technical reasoning. When someone questions an approach, you can explain the trade-offs you considered. You can write code in multiple languages to validate an approach if needed.

2. Code Review & Technical Guidance (30%)

• Full-stack PR reviews: Review Python FastAPI endpoints and Angular components with equal depth, understanding how they interact.
• Deep technical review: Catch issues thoughtful code review can surface:
• RxJS Observable lifecycle and potential memory patterns in Angular
• Query efficiency and data loading patterns in SQLAlchemy
• Terraform module organization and state management implications
• Type safety and TypeScript coverage gaps
• AWS security and IAM configurations
• Educational feedback: Your code reviews help the team learn. When you identify an issue, reviewees understand not just what changed, but how to think about similar problems in the future.
• Define quality expectations: Work with the team to establish what \"production-ready\" means for this platform and support consistent application of those standards.
• What this requires: Experience reviewing code across teams and multiple languages. You know how to write feedback that resonates—clear, constructive, and focused on helping people improve.

3. Mentorship & Team Development (20%)

• Expand specialist capabilities: Help backend specialists learn to contribute to the forms-engine. Support frontend experts in understanding FastAPI patterns.
• Accelerate junior developers: Pair on complex problems. Explain the reasoning behind patterns like DataState. Connect architectural choices to implementation details and performance implications.
• Identify and address gaps: Recognize when someone is struggling with a technology and provide targeted support—training, pair programming, or guidance through architectural decisions.
• Create growth opportunities: Stretch the team into new areas. A backend engineer working on their first Terraform contribution. A frontend specialist implementing an AWS Lambda authorizer.
• What this requires: Genuine investment in people's growth. You've walked developers through major transitions (generalist to specialist, specialist to full-stack, or into new technology areas). You understand that team strength grows when individuals expand their capabilities.

4. Stakeholder Communication & Technical Leadership (10%)

• Explain to diverse audiences: Translate architectural choices and trade-offs for product managers, executives, and business stakeholders. Connect \"optimizing DynamoDB queries\" to \"improving form submission latency by 30%.\"
• Shape technical direction: Contribute the engineering perspective on feasibility, risk, and what unlocks future capabilities.
• Support release confidence: You understand the code changes, comprehend the risks, and know what to monitor. You can stand behind releases.

Required Qualifications

Technical Skills

Frontend (Production Experience)

• 5+ years of Angular (including handling version migrations, optimizing change detection, and guiding teams through reactive patterns)
• Strong TypeScript skills with generics, discriminated unions, and strict mode
• RxJS depth: You understand hot vs. cold observables, unsubscription patterns, and can identify potential memory issues in reviews
• NgRx state management: You've designed stores at scale, optimized selectors, and evaluated architectural implications
• CSS Grid & Responsive Design: You can assess component hierarchy and layout decisions
• Material Design: You've worked within it and know when and how to extend it

Backend (Production Experience)

• 5+ years of Python (async/await, type hints, data modeling)
• FastAPI production experience: session management, dependency injection, middleware
• SQL and ORMs (SQLAlchemy): You write efficient queries and review them critically
• AWS services: Understanding of Lambda behavior, IAM least-privilege patterns, VPC networking
• REST API design: Versioning, error handling, idempotency
• Testing frameworks: pytest, testing st

** We will only consider applicants who are currently residing in South Florida**

About MMG

MMG Equity Partners is a Miami-based, family-led real estate investment and development platform with a portfolio of retail shopping centers across South Florida. Beyond the real estate business, MMG operates a private family office that manages investments, insurance, and financial reporting across multiple entities and family members. MMG separately owns Tamarack Resort in Idaho. We are a flat, fast-moving organization where you will work directly with principals — not layers of management.

This is a ground-floor role. We are building the function from scratch. The right person will define what AI means at MMG, then build it.

The Role

The Director of AI Initiatives & Adoption is responsible for identifying, implementing, and managing AI tools and systems that meaningfully improve how MMG operates across real estate and family office functions. Every project you take on must connect to a business outcome — faster decisions, better data, more deals, reduced overhead.

You will own four things: identifying where AI creates real value at MMG, building or procuring the tools to capture that value, driving adoption across the team and continuously improving how those tools are used, and ensuring the systems are secure and maintainable. Implementation without adoption is not success.

• Reports to Managing Director
• Direct reports - contractors and freelancers as needed
• Current IT Enviroment - outsourced IT for network support

Current Tech Stack (what you are walking into)

You need to understand these systems deeply. Part of your job is figuring out how to connect them and leverage AI to make us more productive/competitive

What you will work on

Below are four areas where we believe AI creates the nearest near-term value at MMG. You first job is to work with the leaders in each area to assess each, prioritize, and build a 6-month roadmap. In addition to the below, the right individual will identify a myriad of other AI use cases to add value and reduce repetitive tasks.

1. Leasing and Tenant Prospecting

MMG owns retail shopping centers and is responsible for filling vacancies with the right tenants – while we work with third party leasing firms, we wish to supplement their efforts by generating direct leads.

• Design and build AI scraping tools to compile databases of South Florida retailers and service businesses for targeted uses
• Build a tool to identify prospective uses/tenants: given a vacancy (size, location, co-tenancy, demographics), which business types and specific operators are the best candidates?
• Design and build AI-assisted leasing outreach workflow: targeted uses identified for vacancies → database queried → outreach drafted and sent → responses tracked in Dynamics (or other CRM)
• Activate Microsoft Dynamics (or other) as the CRM for online leasing
• Identify tools or workflows to monitor existing tenant health (sales reporting, foot traffic, business review signals) to get ahead of vacancies before they happen
• Identify and implement AI-assisted lease abstracting tool to best fit our environment

2. Real Estate Acquisitions

MMG evaluates potential acquisitions across South Florida. Today this process is manual and dependent on individual knowledge. AI can accelerate every stage.

• Design and build AI scraping tools to compile databases of South Florida real estate owners
• Build an AI-assisted underwriting workflow that pulls property data, comps, and market context into a structured analysis template
• Identify AI tools for market intelligence — rent growth trends, cap rate movements, retail category performance by submarket
• Evaluate AI-powered deal sourcing tools (e.g. CoStar integrations, off-market sourcing platforms

3. Private Family Office

MMG's family office manages investments, insurance, and financial reporting for family members. This is a sensitive area requiring strict data governance — but it also has high-value AI applications.

• Addepar AI integration: explore ways to use AI to generate plain-language investment performance summaries and financial reports from Addepar data, reducing manual reporting time
• Insurance management: build a structured database or AI assistant for tracking insurance policies (G/L, personal property, family member policies) with renewal alerts and coverage gap analysis
• Document intelligence: connect family office files in SharePoint to an AI interface for on-demand retrieval of partnership agreements, tax documents, and legal filings
• Evaluate data governance and access controls for family office data — this is sensitive personal and financial information; AI access must be role-based and audited

IT Infrastructure and Security

You are not a network administrator — we have an outsourced IT firm for that. But you are responsible for AI governance at MMG: ensuring every AI tool introduced into the environment meets a clear security and accountability standard.  Practically, this means:

• Evaluating AI vendors for data handling practices — what data leaves our environment, where it is stored, and how it is used for model training
• Defining and enforcing a data classification policy: what information can be sent to external AI APIs, what must stay on-premise or in private cloud environments
• Working with IT firm to ensure AI tools are deployed within the MS365/Azure security perimeter where possible
• Evaluating the Claude Teams → Claude Enterprise migration and the Microsoft Connector configuration for SharePoint access — specifically, controlling which documents are accessible to AI and by which users
• Vetting any third-party AI integrations (i.e. ZoomInfo, Yardi, etc.) for compliance with firm data policies

Prompt Library & AI Adoption

Building the tools is only half the job. The other half is making sure the team actually uses them — and uses them well. This requires two ongoing responsibilities that most AI roles underestimate.

Prompt Library

You will build and maintain a living prompt library — a curated set of tested, optimized prompts for every recurring AI task at MMG. Examples include: underwriting analysis from a rent roll, lease abstraction for a specific clause type, tenant outreach drafts by use category, and insurance renewal gap analysis. The library lives in SharePoint, is accessible to the full team, and is updated continuously based on user feedback and evolving business needs. A well-maintained prompt library is what turns AI from a tool that one person uses well into a capability that the whole organization depends on.

Adoption Monitoring & Continuous Improvement

You are responsible for whether AI tools actually get used — not just whether they get deployed. This means tracking adoption across the team, identifying where workflows are not sticking, providing training and troubleshooting support to staff using AI tools, and iterating on both the tools and the prompts based on real usage patterns. You will serve as the primary internal resource for the team when they hit limitations or need guidance on how to get better outputs. Deployment without adoption is a sunk cost.

What we are looking for

Required:

• 3–6 years of experience in data, technology, or AI — ideally in a context where you had to figure things out without a large team around you
• Hands-on experience with AI tools and LLM platforms — not just using them, but building workflows, prompts, and integrations on top of them
• Demonstrated ability to connect AI capabilities to specific business outcomes (not just technology for its own sake)
• Comfort with the Microsoft 365 ecosystem — SharePoint, Dynamics, Teams, Azure
• Ability to manage and direct contractors and developers without being the one writing all the code
• Non-technical stakeholder communication — you will regularly present AI recommendations, tool evaluations, and implementation roadmaps directly to the principal(s) who are real estate operators, not technologists. The ability to translate AI capabilities into business outcomes (not feature lists) is non-negotiable. If you cannot explain why a tool matters in terms of time saved, deals sourced, or risk avoided, you will not be effective in this role
• In-office presence at Pinecrest HQ is required initially (possible hybrid in the future)

Preferred

• Experience in commercial real estate, property management, or a related field
• Familiarity with Yardi, Addepar, or similar platforms
• Background that includes both technical work (building things) and strategic work (recommending what to build)
• Experience implementing AI in a small-team / resource-constrained environment

AI Innovation Architect

Location: Hybrid; Ashburn, VA; Springfield, VA; Washington, D.C.

Clearance: U.S. Citizen; Must have an active Top-Secret Clearance or DHS Public Trust Clearance.

InDev is seeking a senior strategic and technical AI Architect responsible for designing, building, and deploying artificial intelligence solutions that support mission outcomes across the homeland security market. In this role you will bridge advanced AI capabilities - including machine learning, natural language processing, and data engineering - with operational requirements, ensuring solutions are secure, scalable, and aligned with the homeland security mission.

YOUR FUTURE DUTIES AND RESPONSIBILITIES

• Define overall system architecture, selecting and governing Artificial Intelligence / Machine Learning (AI/ML) and platform technologies, and ensuring solutions are scalable, secure, and production-ready
• Lead end-to-end technical design, development, and implementation of an agentic AI system to orchestrate user queries across enterprise data sources
• Partner closely with development, DevOps and data engineering teams to translate project requirements into an extensible AI architecture
• Create and promote AI strategies that align with business objectives
• Develop and coordinate POCs to test new technologies
• Evaluate and select appropriate AI tools, frameworks, and platforms (i.e., AWS, Azure, Google) to drive innovation

QUALIFICATIONS

• U.S. Citizen; Active Top-Secret Clearance or DHS Public Trust Clearance
• 8+ years of experience delivering AI solutions across federal agencies
• Bachelor’s degree in Computer Science, Engineering, or Data Science
• Deep understanding of machine learning (ML), deep learning, Natural Language Processing (NLP), and neural networks
• Experience with cloud platforms (AWS, Google Cloud, Azure) and container orchestration tools like Kubernetes and Docker
• Ability to identify high-impact AI use cases and translate them into technical requirements
• Experience designing, building, and deploying advanced AI systems including Generative AI, AI Agents, LLMs, Reinforcement Learning, and computer vision models
• Ability to apply cloud and engineering expertise across AWS, GCP, Kubernetes, Docker, Terraform, Helm, Linux, and AI services, such as SageMaker, Vertex AI, Bedrock, or Gemini
• Experience with Python, agent frameworks, data engineering, APIs/microservices, vector databases, SQL engines, distributed systems, cloud services, RAG
• Experience developing and maintaining AI/ML roadmaps, performing Analysis of Alternatives, and making defensible technical tradeoff decisions
• Experience leading multidisciplinary teams, including data scientists, engineers, and business stakeholders
• Excellent written and oral communication skills
• Ability to tailor and present information across multiple stakeholders

NICE TO HAVES

• Experience integrating AI solutions with SaaS/PaaS platforms (e.g., ServiceNow, Salesforce, etc.)
• Experience implementing virtual agents within SaaS/PaaS platforms (e.g., ServiceNow Virtual Agent, Salesforce Agentforce, etc.)
• Experience with Google Gemini

ABOUT US

At InDev, we’re not just a company; we’re a trailblazing force transforming the way data shapes the future. As a dynamic player in the federal government sector, we’re on a mission to empower agencies with cutting-edge data solutions that drive innovation, efficiency, and progress. Our team thrives on collaboration, innovation, and embracing challenges head-on to create a meaningful impact on the world around us.

WHY INDEV

• Innovative Environment: Join a team that thrives on creativity and innovation, where your ideas are not only heard but encouraged.
• Meaningful Impact: Contribute to projects that directly impact federal agencies, driving positive change on a national scale.
• Dynamic Collaboration: Work alongside diverse experts who are passionate about pushing boundaries and making a difference.
• Agile Mindset: Embrace Agile methodologies that encourage flexibility, adaptability, and rapid growth.
• Learning Culture: Enjoy ongoing learning opportunities and professional development to expand your skill set.
• Cutting-edge Tech: Engage with the latest technologies and tools in the data integration landscape.

If you’re ready to embark on a journey of innovation, collaboration, and impact, InDev welcomes you to join our team. Let’s shape the future together.

Project Manager

6 mo. contract to perm hire

Raleigh, NC

Required Skills & Experience

• 5–10 years Project Management experience (ideal)

• Strong technical understanding, ideally with AI / ML / GenAI exposure

• Ability to understand technical details and track progress with engineers & data scientists

• Strong stakeholder communication

• Self-driven, minimal hand-holding

• Experience running multi-team, cross-functional initiatives

Experience managing multiple projects concurrently

Nice to Have Skills & Experience

• Former developer or data scientist background before PM

• Hands-on experience with AI products, AI content pipelines, embeddings

• Experience in B2B environment

Experience in multi-year, large-scale data or ML programs

Job Description

• Manage multiple AI-related projects under the hiring manager's org

• Track progress across engineering + data science teams (~10 people)

• Manage long-term initiatives (multi-year)

• Coordinate embedding strategy project with 20–30+ cross-functional contributors

• Provide weekly updates and progress tracking

• No customer-facing work — only internal stakeholders

• Move projects forward proactively, ensure clarity, keep leadership informed

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to

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