Ai Address Validation Jobs in Usa

At Rite-Hite, your work makes an impact. As the global leader in loading dock and door equipment, we design and deliver solutions that keep our customers safe, secure, and productive. Here, you'll find innovation, stability, and the chance to grow your career as part of a team that's always looking ahead.

ESSENTIAL DUTIES AND RESPONSIBILITIES

To perform this job successfully, an individual must be able to perform each essential duty satisfactorily.

• Design and build AI-powered applications using Large Language Models (LLMs) for enterprise use cases.
• Develop Retrieval-Augmented Generation (RAG) solutions using structured and unstructured enterprise data such as documents, manuals, tickets, ERP data, and knowledge bases.
• Build and orchestrate AI agents that can reason, plan, and interact with tools, APIs, and workflows.
• Implement guardrails for AI systems including prompt safety, data protection, hallucination mitigation, access control, and output validation.
• Work with multimodal AI models including text, image, and video use cases such as video analysis, summarization, and optimization.
• Integrate AI solutions with existing enterprise systems such as Salesforce, ERP platforms, data lakes, APIs, and internal applications.
• Partner with security and compliance teams to ensure responsible AI usage, data privacy, and governance.
• Prototype quickly, then harden solutions for production with monitoring, logging, evaluation, and performance optimization.
• Mentor and upskill existing developers on AI concepts, patterns, and best practices.

Required Skills & Experience

• 5+ year of full stack development experience.
• Strong software engineering background with experience building production-grade applications.
• Hands-on experience with modern LLM platforms such as OpenAI, Azure OpenAI, Anthropic, or similar.
• Practical experience building RAG pipelines using vector databases and embedding models.
• Experience with prompt engineering, prompt versioning, and evaluation techniques.
• Solid Python experience for AI development.
• Experience integrating AI services with REST APIs, microservices, and cloud-native architectures.
• Familiarity with cloud platforms such as AWS or Azure, including deployment, scaling, and security concepts.
• Understanding of data formats such as JSON, XML, and document-based data.
• Ability to translate business problems into AI-driven technical solutions.

Preferred Qualifications

• Experience with vector databases such as Pinecone, FAISS, Weaviate, or similar.
• Familiarity with frameworks such as LangChain, LlamaIndex, Semantic Kernel, or equivalent orchestration tools.
• Experience implementing AI safety controls, policy enforcement, and evaluation frameworks.
• Exposure to video or image models and multimodal AI use cases.
• Experience working in enterprise environments with security, compliance, and change management considerations.
• Prior experience mentoring or leading developers in new technical domains.

What We Offer

At Rite-Hite, we take care of our people - because when you're supported, you can do your best work. Our benefits are designed to support your health, your future and your life outside of work:

• Health & Well-being: Comprehensive medical, dental, and vision coverage, plus life and disability insurance. A robust well-being program with an opportunity to receive an extra day off and more.

• Financial Security: A strong retirement savings program with 401(k), company match, and profit sharing.

• Time for You: Paid holidays, vacation time, and personal/sick days each year.

Join us and build a career where you're supported - at work and beyond.

Rite-Hite is proud to be an Equal Opportunity Employer. We consider all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic under federal, state, or local law.In accordance with VEVRAA, we are committed to providing equal employment opportunities for protected veterans.We are also committed to maintaining a drug-free workplace for the safety of our employees and customers.

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.

Role : Enterprise AI leader (Manufacturing domain)

Location : Cleveland OH (Hybrid)

Fulltime role

Role Overview

We are seeking a highly skilled and proactive Enterprise AI consultant for Manufacturing to drive AI transformation and business intelligence initiatives. This role requires close collaboration with IT leaders, site-level business stakeholders, value stream leaders, and end-user personas to identify, design, and deliver high-impact AI use cases and BI solutions. Below are the MUST have skills

• Enterprise-wide data & AI architecture strategy
• Cloud modernization and scalable platform design
• Proven leadership in large-scale data modernization and OT/IT transformation
• Integration of historians, MES/SCADA, PLCs, IoT, and enterprise systems
• Experience in manufacturing shop floor functionality, operational workflow, supply chain
• Expertise in Snowflake, cloud platforms, AI/ML, industrial data systems
• Proven success driving large-scale digital transformation
• Translate business challenges into structured AI opportunities and build scalable solutions in collaboration with data engineering and data science teams.
• Good understanding of AI/ML concepts, GenAI frameworks, and data engineering fundamentals. Experience in driving proof of concept in AI use cases
• Conduct PoC development, solution validation, and oversee production deployment of AI models and automation use cases.

Senior Developer, AI Evaluation & Cloud Infrastructure | Just Horizons Alliance

Join us to build the technical foundation for AI accountability.

The Role

Just Horizons Alliance is an 18-year-old applied research lab focused on ethics and technology. Our current focus is the AI Ethics Index, a measurement framework for evaluating AI systems on ethics, safety, and societal impact.

We need a senior engineer to own the technical infrastructure end-to-end: learn what exists, close critical gaps, and build something that lasts.

The evaluation methodology is validated and in use. We're now at the stage where the systems need to mature alongside the research. This is the first dedicated infrastructure hire for this work, and you'll shape how it scales.

What You'll Do

Months 1–3: Learn the System

Map the current architecture with Sophia Zitman (AIEI Team Lead). Understand the evaluation methodology, the data flows, and the infrastructure that supports them. Identify what needs to evolve for multi-domain benchmarking—reproducibility, security posture, test coverage, deployment pipeline. Begin implementing the highest-priority improvements.

Months 4–6: Build for Scale

Architect the infrastructure to support the next phase of the Index. CI/CD that maintains stability as the system grows. IAM and secret management built for a production environment. Experiment tracking that makes every evaluation run auditable. Documentation that enables the research team to work independently.

Months 7–12: Expand

Multi-domain benchmarking across education, healthcare, finance, and other sectors. Reproducibility standards that meet external scientific scrutiny. A system the research team can extend without engineering support for every change. At this point, the infrastructure should be stable enough that you're focused on capability, not maintenance.

Why This Role Is Difficult

This is infrastructure for a scientific standard, not a product feature.

Correctness and delivery both matter. A bug in the evaluation engine doesn't break a feature, instead it invalidates a measurement. A flawed pipeline doesn't slow things down, it compromises the credibility of the research. At the same time, methodology that never runs in production has no impact. The role requires both rigor and momentum.

You're translating between disciplines. Your stakeholders are researchers, ethicists, and governance specialists. You'll need to take concepts like \"operationalizing an ethical construct\" and turn them into data models and pipelines. This is a translation problem as much as an engineering problem.

The work is novel. There's no existing system to reference. The AI Ethics Index is defining what rigorous AI evaluation looks like. You'll be making architectural decisions in areas where best practices don't yet exist.

You'll have full ownership. This is not a role where you're executing someone else's technical vision. You're setting the direction. That means autonomy, but it also means accountability.

You're probably the right person if

You've built evaluation systems or data pipelines that other people depended on for correctness, not just uptime

You're comfortable with GCP and have deployed containerized workloads in a real production context

You've worked with LLM APIs and understand their reliability and reproducibility characteristics

You can read a paper about measurement methodology and turn it into a working data structure

You build for durability. Your code is still running 18 months later because you thought about the next person

You've worked somewhere between 5 and 50 people and you're comfortable being the person who figures things out without a playbook

You find working on AI ethics infrastructure more interesting than building another e-commerce checkout flow

You're probably not the right fit if

Enterprise environments make up most of your experience. This is not a large-team context

You need clearly defined requirements before you can start. The requirements here evolve through conversation with ethicists

You're based on the West Coast US or expect West Coast US working hours

You mainly build user-facing APIs and features — this is systems and infrastructure work

You're looking for a high-growth startup where shipping speed is everything. This is a scientific organization. Correctness is everything.

Hard Skills

These are the technical capabilities you need going in — or need to be able to build up fast using an AI coding agent. We're not looking for someone who ticks every box. We're looking for someone who closes gaps quickly and knows how to learn.

• Python — strong enough to design systems architecture and reason about failure modes, even if you work with AI assistance for implementation details
• Google Cloud Platform — specifically Cloud Run, IAM design, secret management, and containerized workload deployment in a real production context
• API and model documentation — able to read, write, and navigate API specs and model documentation fluently; you know how to figure out how a system behaves from its documentation without needing someone to walk you through it
• Structured step-by-step reasoning — when you hit a complex problem, you decompose it immediately and visibly into logical steps; you don't disappear into your head and come back with an answer, you think out loud and in sequence, which makes collaboration with the ethics and research team possible
• LLM API integration — understanding the reliability, reproducibility, and failure characteristics of external model endpoints
• Data pipeline architecture — building evaluation or measurement systems where correctness is non-negotiable, not just data-moving
• Experiment tracking and reproducibility standards — always looking to improve the evaluation pipeline; you understand what needs to be tracked, why reproducibility matters scientifically, and you find the right approach for the context without being dogmatic about tooling
• Statistical reliability concepts — enough to understand what inter-rater reliability means for evaluation output and why reproducibility matters scientifically

What you get

The role: You'll work directly with Sophia Zitman (AIEI Team Lead) as the technical backbone of the AI Ethics Index. Full engineering ownership of the evaluation engine.

The comp: Base salary $110,000.

The team: Small, split between ethicists and engineers. You will interview with Janet Kang (Executive Director) and Sophia Zitman (AIEI Team Lead).

The environment: Boston-based non-profit (501(c)(3)). East Coast US or Western Europe time zones. Collaborative but autonomous — Sophia won't micromanage, but she will hold you to a high standard of systems thinking.

The upside: You'll have built the technical foundation of what may become the globally referenced standard for AI system evaluation. That's a meaningful line on any CV — and a genuinely hard thing to have done.

Why Zensar?

We’re a bunch of hardworking, fun-loving, people-oriented technology enthusiasts. We love what we do, and we’re passionate about helping our clients thrive in an increasingly complex digital world. Zensar is an organization focused on building relationships with our clients and with each other—and happiness is at the core of everything we do. In fact, we’re so into happiness that we’ve created a Global Happiness Council, and we send out a Happiness Survey to our employees each year. We’ve learned that employee happiness requires more than a competitive paycheck, and our employee value proposition—grow, own, achieve, learn (GOAL)—lays out the core opportunities we seek to foster for every employee. Teamwork and collaboration are critical to Zensar’s mission and success, and our teams work on a diverse and challenging mix of technologies across a broad industry spectrum. These industries include banking and financial services, high-tech and manufacturing, healthcare, insurance, retail, and consumer services. Our employees enjoy flexible work arrangements and a competitive benefits package, including medical, dental, vision, 401(k), among other benefits. If you are looking for a place to have an immediate impact, to grow and contribute, where we work hard, play hard, and support each other, consider joining team Zensar!

QA / Quality Engineering Delivery Lead

Location: Secaucus, NJ (Hybrid – 3 days onsite)

Employment Type: Full-time / Contract

Experience: 12–15 years

Domain: Retail

Role Overview

We are seeking a QA / Quality Engineering Delivery Lead to own end-to-end quality delivery while driving QE transformation and modernization initiatives, including AI-augmented testing and intelligent automation frameworks. This role demands a tool-agnostic automation mindset, strong leadership capabilities, and the ability to balance BAU delivery with future-ready QE transformation, leveraging GPT-based testing and AI-led quality practices.

Key Responsibilities:

• Own quality outcomes across programs, releases, and product lines
• Lead day-to-day BAU QA delivery, including:
• Test planning & execution
• Defect management
• Release validation and go/no-go readiness
• Drive QE assessments and build continuous improvement & transformation roadmaps
• Define and execute modern test automation strategies across:
• UI, API, Mobile, and End-to-End (E2E) automation
• Lead AI-augmented testing initiatives, including:
• GPT/LLM-based test case generation
• Intelligent test design and risk-based testing
• Self-healing automation and test optimization
• Promote shift-left and shift-right testing by partnering with:
• Product Management
• Engineering
• DevOps and SRE teams
• Embed quality early in the SDLC through CI/CD and cloud-native testing
• Establish and track quality metrics, KPIs, and dashboards
• Provide clear visibility into quality status, risks, and dependencies for senior stakeholders
• Mentor QA/QE teams and foster a continuous improvement and innovation culture.

Required Skills & Experience

Must Have

• 10–14 years of experience in QA / Quality Engineering
• Proven leadership experience managing QA/QE teams in Agile & DevOps environments
• Strong hands-on expertise in test automation frameworks, including:
• Selenium, Playwright, Cypress (any one or more)
• Exposure to Tricentis Tosca (preferred but not mandatory)
• Solid experience in:
• API & integration testing
• Test data management
• Defect lifecycle management
• Demonstrated experience conducting:
• QE maturity assessments
• Automation ROI analysis
• QE transformation planning
• Ability to manage BAU delivery alongside modernization and innovation initiatives
• Strong Retail domain experience (POS, eCommerce, supply chain, merchandising systems preferred)

AI-Augmented & Intelligent QE (Mandatory Focus)

• Hands-on or leadership experience with AI-driven QE practices, including:
• GPT / LLM-based test case & test scenario generation
• AI-assisted exploratory testing
• Intelligent test selection, prioritization, and impact analysis
• Experience building or adopting intelligent automation frameworks with:
• Self-healing capabilities
• Dynamic locators & adaptive scripts
• Familiarity with:
• Generative AI usage in QE pipelines
• Prompt engineering for test generation
• Ability to operationalize AI in QE, not just PoCs

Zensar believes that diversity of backgrounds, thought, experience, and expertise fosters the robust exchange of ideas that enables the highest quality collaboration and work product. Zensar is an equal opportunity employer. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by federal, state, or local law. Zensar is committed to providing veteran employment opportunities to our service men and women. Zensar is committed to providing equal employment opportunities for people with disabilities or religious observances, including reasonable accommodation when needed. Accommodation made to facilitate the recruiting process are not a guarantee of future or continued accommodation once hired.

All applicants must be legally authorized to work with Zensar. Visa sponsorship may be available for qualified applicants for certain positions.

Zensar values your privacy. We’ll use your data in accordance with our privacy statement located at:

About Wakefern

Wakefern Food Corp. is the largest retailer-owned cooperative in the United States and supports its co-operative members' retail operations, trading under the ShopRite®, Price Rite®, The Fresh Grocer®, Dearborn Markets®, and Gourmet Garage® banners.

Employing an innovative approach to wholesale business services, Wakefern focuses on helping the independent retailer compete in a big business world. Providing the tools entrepreneurs need to stay a step ahead of the competition, Wakefern’s co-operative members benefit from the company’s extensive portfolio of services, including innovative technology, private label development, and best-in-class procurement practices.

The ideal candidate will have a strong background in designing, developing, and implementing complex projects, with focus on automating data processes and driving efficiency within the organization. This role requires a close collaboration with application developers, data engineers, data analysts, data scientists to ensure seamless data integration and automation across various platforms. The Data Integration & AI Engineer is responsible for identifying opportunities to automate repetitive data processes, reduce manual intervention, and improve overall data accessibility.

Essential Functions

• Participate in the development life cycle (requirements definition, project approval, design, development, and implementation) and maintenance of the systems.
• Implement and enforce data quality and governance standards to ensure the accuracy and consistency.
• Provide input for project plans and timelines to align with business objectives.
• Monitor project progress, identify risks, and implement mitigation strategies.
• Work with cross-functional teams and ensure effective communication and collaboration.
• Provide regular updates to the management team.
• Follow the standards and procedures according to Architecture Review Board best practices, revising standards and procedures as requirements change and technological advancements are incorporated into the >tech_ structure.
• Communicates and promotes the code of ethics and business conduct.
• Ensures completion of required company compliance training programs.
• Is trained – either through formal education or through experience – in software / hardware technologies and development methodologies.
• Stays current through personal development and professional and industry organizations.

Responsibilities

• Design, build, and maintain automated data pipelines and ETL processes to ensure scalability, efficiency, and reliability across data operations.
• Develop and implement robust data integration solutions to streamline data flow between diverse systems and databases.
• Continuously optimize data workflows and automation processes to enhance performance, scalability, and maintainability.
• Design and develop end-to-end data solutions utilizing modern technologies, including scripting languages, databases, APIs, and cloud platforms.
• Ensure data solutions and data sources meet quality, security, and compliance standards.
• Monitor and troubleshoot automation workflows, proactively identifying and resolving issues to minimize downtime.
• Provide technical training, documentation, and ongoing support to end users of data automation systems.
• Prepare and maintain comprehensive technical documentation, including solution designs, specifications, and operational procedures.

Qualifications

• A bachelor's degree or higher in computer science, information systems, or a related field.
• Hands-on experience with cloud data platforms (e.g., GCP, Azure, etc.)
• Strong knowledge and skills in data automation technologies, such as Python, SQL, ETL/ELT tools, Kafka, APIs, cloud data pipelines, etc.
• Experience in GCP BigQuery, Dataflow, Pub/Sub, and Cloud storage.
• Experience with workflow orchestration tools such as Cloud Composer or Airflow
• Proficiency in iPaaS (Integration Platform as a Service) platforms, such as Boomi, SAP BTP, etc.
• Develop and manage data integrations for AI agents, connecting them to internal and external APIs, databases, and knowledge sources to expand their capabilities.
• Build and maintain scalable Retrieval-Augmented Generation (RAG) pipelines, including the curation and indexing of knowledge bases in vector databases (e.g., Pinecone, Vertex AI Vector Search).
• Leverage cloud-based AI/ML platforms (e.g., Vertex AI, Azure ML) to build, train, and deploy machine learning models on a scale.
• Establish and enforce data quality and governance standards for AI/ML datasets, ensuring the accuracy, completeness, and integrity of data used for model training and validation.
• Collaborate closely with data scientists and machine learning engineers to understand data requirements and deliver optimized data solutions that support the entire machine learning lifecycle.
• Hands-on experience with IBM DataStage and Alteryx is a plus.
• Strong understanding of database design principles, including normalization, indexing, partitioning, and query optimization.
• Ability to design and maintain efficient, scalable, and well-structured database schemas to support both analytical and transactional workloads,
• Familiarity with BI visualization tools such as MicroStrategy, Power BI, Looker, or similar.
• Familiarity with data modeling tools.
• Familiarity with DevOps practices for data (CI/CD pipelines)
• Proficiency in project management software (e.g., JIRA, Clarizen, etc.)
• Familiarity with DevOps practices for data (CI/CD pipelines)
• Strong knowledge and skills in data management, data quality, and data governance.
• Strong communication, collaboration, and problem-solving skills.
• Ability to work on multiple projects and prioritize tasks effectively.
• Ability to work independently and in a team environment.
• Ability to learn new technologies and tools quickly.
• The ability to handle stressful situations.
• Highly developed business acuity and acumen.
• Strong critical thinking and decision-making skills.

Working Conditions & Physical Demands

This position requires in-person office presence at least 4x a week.

Compensation and Benefits

The salary range for this position is $75,868 - $150,644. Placement in the range depends on several factors, including experience, skills, education, geography, and budget considerations.

Wakefern is proud to offer a comprehensive benefits package designed to support the health, well-being, and professional development of our Associates. Benefits include medical, dental, and vision coverage, life and disability insurance, a 401(k) retirement plan with company match & annual company contribution, paid time off, holidays, and parental leave.

Associates also enjoy access to wellness and family support programs, fitness reimbursement, educational and training opportunities through our corporate university, and a collaborative, team-oriented work environment. Many of these benefits are fully or partially funded by the company, with some subject to eligibility requirements

AI Research Scientist | Machine Learning | Deep Learning | Natural Language Processing | LLM | Hybrid | San Jose, CA

Title: AI Research Scientist

Location: San Jose, CA

Responsibilities:

• Design, execute, and analyze machine learning experiments, establishing strong baselines and selecting appropriate evaluation metrics.
• Stay up to date with the latest AI research; identify, adapt, and validate novel techniques for company-specific use cases.
• Define rigorous evaluation protocols, including offline metrics, user studies, and adversarial (red team) testing to ensure statistical soundness.
• Specify data and annotation requirements; develop annotation guidelines and oversee quality control processes.
• Collaborate closely with domain experts, product managers, and engineering teams to refine problem statements and operational constraints.
• Develop reusable research assets such as datasets, modular code components, evaluation suites, and comprehensive documentation.
• Work alongside ML Engineers to optimize training and inference pipelines, ensuring seamless integration into production systems.
• Contribute to academic publications and represent the company in research communities, as needed.

Educational Qualifications:

• Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related field is strongly preferred.
• Candidates with a master’s degree and exceptional research or industry experience will also be considered.

Industry Experience:

• 3–5 years of experience in AI/ML research roles, ideally in applied or product-focused environments.
• Demonstrated success in delivering research-driven solutions that have been deployed in production.
• Experience collaborating in cross-functional teams across research, engineering, and product.
• Publications in top-tier AI/ML conferences (e.g., NeurIPS, ICML, ACL, CVPR) are a plus.

Technical Skills:

• Strong foundational knowledge in machine learning and deep learning algorithms.
• Hands-on experience with PEFT/LoRA, adapters, fine-tuning techniques, and RLHF/RLAIF (e.g., PPO, DPO, GRPO).
• Ability to read, implement, and adapt state-of-the-art research papers to real-world use cases.
• Proficiency in hypothesis-driven experimentation, ablation studies, and statistically sound evaluations.
• Advanced programming skills in Python (preferred), C++, or Java.
• Experience with deep learning frameworks such as PyTorch, Hugging Face, NumPy, etc.
• Strong mathematical foundations in probability, linear algebra, and calculus.
• Domain expertise in one or more areas: natural language processing (NLP), symbolic reasoning, speech processing, etc.
• Ability to translate research insights into roadmaps, technical specifications, and product improvements.

AI Research Scientist | Machine Learning | Deep Learning | Natural Language Processing | LLM | Hybrid | San Jose, CA

** 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

GENERAL SUMMARY:

The Manager of AI Enablement (Senior) leads the development and execution of Element Care’s internal approach to artificial intelligence. This role defines AI standards, policies, and best practices while enabling staff across the organization to adopt AI safely, ethically, and effectively. Reporting to the IT department, this position acts as a trusted advisor to leaders and end users, shaping AI governance, vendor strategy, training, and enterprise enablement.

ESSENTIAL RESPONSIBILITIES:

•    Define and maintain organizational AI standards, policies, and governance frameworks.

•    Lead the deployment of off-the-shelf AI solutions, including ambient documentation, predictive analytics, administrative automation, and clinical decision support tools.

•    Enable responsible use of generative AI across administrative and operational functions.

•    Conduct continuous workflow analysis to identify automation and AI-enablement opportunities.

•    Evaluate AI and AI/ML models, tools, and vendor solutions for suitability, risk, and value.

•    Partner with IT, data, analytics, and platform teams to align AI initiatives with enterprise architecture.

•    Provide oversight and guidance on AI-enabled workflows, automation, and agent capabilities.

•    Measure, monitor, and report on AI initiative outcomes, value realization, and performance.

•    Build business cases and recommendations for future AI investments.

•    Serve as the primary advisor to leaders and teams on AI use cases, risks, and governance.

•    Monitor regulatory, ethical, and industry developments related to AI.

•    Help establish and mature a scalable AI enablement and governance operating model.

•    Influence adoption and consistency without direct authority.

•    Perform other duties as assigned.

JOB SPECIFICATION:

•    6–10+ years of relevant professional experience, including applied AI, automation, analytics, or emerging technology leadership.

•    Demonstrated experience evaluating AI/ML models, vendor solutions, or AI platforms.

•    Experience with vendor management, solution selection, or hands-on implementation required.

•    Demonstrated experience defining standards, policies, or enterprise enablement programs.

•    Healthcare or other regulated industry experience strongly preferred.

•    Strong understanding of applied AI, AI/ML evaluation, governance, risk, and ethical considerations.

•    Ability to translate complex concepts into practical organizational guidance.

•    Experience developing business cases and value narratives for technology investments.

•    Executive-level communication and facilitation skills.

•    Proven ability to operate independently and influence across the enterprise.

•    Strategic mindset with a pragmatic, implementation-oriented approach.

Compensation details: 13 Yearly Salary

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