Principal AI Platform Engineer

The Opportunity

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 let teams 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. The role is Director-mapped and begins as a player-coach, with responsibility to grow and lead a platform engineering team.

What You'll Do

• 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 that supports multi-provider routing, failover, quota and rate limiting, tenant isolation, key management, token accounting, and cost allocation (e.g., a LiteLLM-style gateway over Azure OpenAI, Amazon Bedrock, or Vertex AI).
• 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 platform observability and traceability for prompts, model calls, tool calls, and agent steps using distributed tracing (e.g., OpenTelemetry GenAI semantic conventions and Langfuse- or Arize-class tooling), with evaluation, quality, drift, latency, and cost signals.
• Implement security, guardrails, and responsible-AI controls in the platform, 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 pragmatic build-versus-buy and vendor recommendations with budget input.
• Hire, coach, and develop a platform engineering team of approximately 3–8 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.

Required Qualifications

• 10–15+ years of software engineering, platform engineering, distributed systems, or infrastructure experience, including significant technical leadership.
• Hands-on experience designing and operating enterprise AI/GenAI platforms or comparable developer, data, or ML platforms in production.
• Strong software engineering ability in Python and at least one of TypeScript, Go, or Java, with experience in APIs, distributed systems, testing, code review, and secure SDLC.
• Deep technical understanding of model gateways, GenAI runtimes, RAG, agent registries, evaluation, observability, traceability, guardrails, identity, and platform governance.
• Experience with containers and orchestration (e.g., Kubernetes), infrastructure as code, CI/CD, cloud services, secrets management, and production SLOs and on-call practices.
• Bachelor's degree in a related field or equivalent professional experience.
• Demonstrated ability to lead technical decisions, influence across teams, communicate clearly with senior stakeholders, and manage a growing engineering team.

Preferred Qualifications

• 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.