Technical Lead/Architect - AI Platforms

TEKsystems•North Chicago, IL

5d•$100 - $120•Remote

About The Position

A hands-on technical leadership role accountable for the architecture, engineering, and reliability of Abbott's enterprise AI Platform—the control plane and paved road that enable teams to build, deploy, and operate enterprise GenAI and agentic systems safely and quickly. This role is the technical counterpart to the Product Manager, AI Platform and partners with AI Product Managers, AI/Agentic Engineering, Applied Data Science, security, and division engineering teams. It is Director-mapped and begins as a player-coach, with responsibility for growing and leading a platform engineering team. The role defines and evolves the reference architecture for the AI control plane, paved-road services, SDKs, templates, and developer experience. It designs and builds a model gateway supporting multi-provider routing, failover, quotas and rate limits, tenant isolation, key management, token accounting, and cost allocation. It delivers a GenAI runtime for model serving, retrieval-augmented generation, context and memory stores, vector and hybrid search, sandboxed tool or code execution, secrets brokering, and MCP-style tool interoperability. The position owns the technical design of model, agent, prompt, and tool registries, including versioning, lineage, approval and promotion gates, deprecation, and auditability. It builds platform observability and traceability for prompts, model calls, tool calls, and agent steps, with evaluation and quality, drift, latency, and cost signals. It implements security, guardrails, and responsible-AI controls, and establishes platform engineering practices using Kubernetes, infrastructure as code, CI/CD, secure software development, capacity management, and reliability engineering. The role defines SLOs, participates in on-call, and leads incident response. It writes and reviews production code, conducts architecture reviews, sets engineering standards, and makes build-versus-buy and vendor recommendations with budget input. It hires, coaches, and develops a platform engineering team of approximately 38 engineers, allocating work, setting goals, and managing career development while remaining technically hands-on. It partners with the Product Manager, AI Platform to sequence the roadmap, communicate tradeoffs, and measure adoption, reliability, time-to-onboard, and consumer-team outcomes.

Requirements

Experience building enterprise GenAI or agent platforms using capabilities such as multi-model routing, agent registries, prompt management, evaluation harnesses, and GenAI tracing.
Experience in a highly regulated environment such as health tech, med-tech, or life sciences, with GxP, HIPAA, validation, auditability, and change-control constraints.
Experience implementing security, responsible-AI, privacy, policy, and cost or FinOps controls for shared AI infrastructure.
Experience with open standards and tooling such as MCP, OpenTelemetry, Kubernetes, and cloud-native platform engineering practices.

Responsibilities

Define and evolve the reference architecture for the AI control plane, paved-road services, SDKs, templates, and developer experience.
Design and build a model gateway supporting multi-provider routing, failover, quota and rate limiting, tenant isolation, key management, token accounting, and cost allocation.
Deliver a GenAI runtime for model serving, retrieval-augmented generation, context and memory stores, vector and hybrid search, sandboxed tool or code execution, secrets brokering, and MCP-style tool interoperability.
Own the technical design of model, agent, prompt, and tool registries, including versioning, lineage, approval and promotion gates, deprecation, and auditability.
Build observability and traceability for prompts, model calls, tool calls, and agent steps, with evaluation, quality, drift, latency, and cost signals.
Implement platform security, guardrails, and responsible-AI controls, including PII/PHI handling, prompt-injection defenses, identity-on-behalf-of, least-privilege tool access, and policy enforcement.
Establish platform engineering practices using Kubernetes, infrastructure as code, CI/CD, secure software development, capacity management, and reliability engineering.
Define SLOs, participate in on-call, and lead incident response.
Write and review production code, conduct architecture reviews, set engineering standards, and make build-versus-buy and vendor recommendations with budget input.
Hire, coach, and develop a platform engineering team of approximately 38 engineers; allocate work, set goals, and manage career development while remaining technically hands-on.
Partner with the Product Manager, AI Platform to sequence the platform roadmap, communicate tradeoffs, and measure adoption, reliability, time-to-onboard, and consumer-team outcomes.