Hansell Tierney Jobs in Usa

Our client, a large, complex enterprise organization, is looking for a Principal AI Engineer to serve as the lead architect and hands-on builder of a unified AI Platform as a Service (PaaS) a secure, multi-tenant foundation that helps internal teams build and operate semantic discovery, conversational experiences, and autonomous agent workflows at scale.

Summary:

The Principal AI Engineer will design and build an enterprise-grade “AI operating layer” that turns modern foundation model capabilities into a governed, reusable platform used across multiple business domains. This role balances approximately 40% hands-on development with 60% platform strategy, personally building core orchestration services, standardized capability interfaces, and trust/safety guardrails. The platform will enable teams to deploy specialized agents that can collaborate via defined protocols, securely access grounded knowledge sources, and execute autonomous tasks within a controlled, high-availability runtime.

Location: Remote (U.S.) / Hybrid options may be available based on client needs.

Compensation Range: $145,000 - $250,000 per year plus RSUs & Bonus

Benefits: Very competitive benefits

Responsibilities

• Architect and deliver a self-service AI platform that provides reusable patterns, reference implementations, and standardized building blocks for internal engineering teams.
• Define and communicate a multi-year platform roadmap, ensuring technical priorities map to enterprise outcomes and adoption goals.
• Design and implement stateful orchestration (state graphs/state machines) to handle planning edge cases, recovery, and self-correction in autonomous workflows.
• Build and operate secure remote tool gateways (e.g., MCP-style servers) and implement controlled function-calling interfaces for connecting agents to sensitive enterprise systems.
• Establish interoperability standards for agent-to-agent collaboration, enabling autonomous discovery and reliable task handoffs across independently built agent solutions.
• Design an agent identity and authorization layer that supports fine-grained permissions, auditable actions, and strong accountability for autonomous behaviors.
• Implement a unified knowledge layer using semantic retrieval and multimodal grounding to support accurate, “source-aligned” responses and decisions.
• Build long-term context persistence (“memory”) using retrieval and graph-based storage to preserve institutional knowledge and improve continuity over time.
• Create a trust and evaluation layer with automated testing pipelines to measure quality, safety, cost, latency, and reliability of agent behavior across tenants.
• Own runtime lifecycle management for agent sessions, ensuring high availability, persistence, scalability, and controlled rollout patterns.
• Lead deep code reviews focused on agent-specific failure modes (runaway loops, tool misuse, state growth, unreliable calling patterns) and implement mitigations.
• Optimize inference performance and spend through techniques such as prompt caching, model routing, and workload-aware runtime strategies.
• Act as a technical multiplier by mentoring senior/staff engineers on advanced agentic patterns, evaluation methods, and production hardening.
• Partner closely with Cloud and Infrastructure teams to influence enabling services and platform primitives needed for enterprise AI delivery.
• Raise the bar on engineering quality via documentation, profiling, reliability improvements, and ongoing performance tuning.

QUALIFICATIONS:

Required:

• 10+ years of software engineering experience, including 4+ years operating at a Principal/Architect level.
• 2+ years architecting and shipping LLM-based systems, with demonstrated experience taking agentic solutions into production at scale.
• 5+ years working in agile delivery environments.
• Google Cloud Professional Cloud Architect certification.
• Proven ability to lead technical workstreams and translate business needs into durable platform architectures.
• Strong expertise in asynchronous orchestration (e.g., Python) plus proficiency in a statically typed language (Java, Go, or Rust) for high-concurrency platform services.
• Hands-on experience with stateful graph orchestration patterns and frameworks (e.g., LangGraph/ADK-style approaches) to power robust reasoning workflows.
• Strong cloud-native experience with CLI tooling and Infrastructure-as-Code; proven ability to deploy and scale containerized workloads using container orchestration and serverless platforms.
• Experience designing and operating distributed architectures at scale, including vector stores, graph databases, and structured data pipelines.
• Deep knowledge of multi-agent design patterns and the ability to extend or replace off-the-shelf orchestration when scaling, safety, or reliability requires it.
• Working understanding of modern agent reasoning approaches (Chain-of-Thought, ReAct, Tree-of-Thoughts, Self-Reflection) and when to apply them.
• Experience supporting very high request volumes and/or extremely large datasets in production environments.
• Expertise building semantic retrieval layers, attribute-aware discovery, and stateful persistence for long-lived agent context.
• Strong understanding of MCP-style tool protocols, agent-to-agent interaction patterns, REST/gRPC APIs, OAuth2, and function-calling mechanics.
• Familiarity with microservices architecture patterns and distributed systems best practices.
• Ability to implement observability for agentic systems, including traceability/telemetry and debugging methods for multi-step reasoning and handoffs.
• Awareness of global AI regulations (e.g., EU AI Act) and ability to translate requirements into technical controls and platform governance.
• Strong written and verbal communication skills across technical and business audiences.
• Calm, decisive execution in high-pressure or incident scenarios.
• Highly organized, self-directed, detail-oriented, and effective with limited supervision.
• Ability to support off-hours work as needed, including rotational on-call, weekends, and holidays.

Preferred:

• Bachelor’s or Master’s in Computer Science, AI, or a related field.
• PhD in AI, Distributed Systems, or Cognitive Science.
• Google Cloud Professional Machine Learning Engineer certification and/or specialized certifications in multi-agent systems/autonomous reasoning.
• 3+ years working with distributed caching technologies.
• Experience provisioning and configuring cloud platform resources at scale.
• Experience supporting consumer-facing digital or commerce environments.
• Proficiency with Google Workspace (Sheets, Docs, Slides, Gmail).

About Hansell Tierney:

Hansell Tierney is one of the premier staffing and recruiting companies in the Pacific Northwest. Launched in 2001, we are a woman-owned business that serves and staffs Northwest organizations by doing things the right way, not just the easiest way. Hansell Tierney partners with candidates and clients to match the best candidates with interesting local opportunities. We navigate every relationship with the highest level of discretion and service while holding ourselves accountable to our promises. Our business thrives on our deep understanding of the job market and our ability to skillfully tailor our recruitment process to meet our clients’ unique needs.