Principal AI Platform Engineer

Abbott; Illinois; On-site; USD 149,000 - 299,000; Full time

Job summary

Abbott, a global healthcare leader, seeks a hands-on technical leader to architect and run the enterprise AI Platform, enabling teams to build, deploy, and operate GenAI and agentic systems safely and quickly.

You will shape the control plane, implement robust gateways, and lead a small platform team while partnering with AI Product Managers and engineering teams to deliver scalable, compliant AI infrastructure.

Qualifications

• 10–15+ years in software engineering or platform engineering with leadership experience.
• Hands-on experience designing and operating enterprise AI/GenAI platforms in production.
• Strong Python and at least one of TypeScript, Go, or Java, with API and distributed-systems work.
• Deep understanding of model gateways, GenAI runtimes, evaluation, observability, guardrails, and governance.
• Experience with Kubernetes, IaC, CI/CD, cloud services, secrets management, and on-call practices.
• Bachelor's degree or equivalent professional experience.
• Proven ability to lead technical decisions and grow a team.

Responsibilities

• Define and evolve the reference architecture for the AI control plane and developer experience.
• Design and build a model gateway with multi-provider routing, quota and cost accounting.
• Deliver a GenAI runtime for model serving, retrieval-augmented generation, and tool interoperability.
• Own design of model, agent, prompt, and tool registries with versioning and auditability.
• Build platform observability and traceability using OpenTelemetry and related tooling.
• Implement security, guardrails, and responsible-AI controls within the platform.
• Establish platform engineering practices including Kubernetes, IaC, CI/CD, and reliability engineering.
• Lead a platform engineering team of 3–8 engineers; mentor and develop careers while remaining hands-on.
• Collaborate with product managers to sequence platform roadmap and measure adoption and reliability.

Skills

Python

TypeScript

Go

Java

Distributed systems

Kubernetes

CI/CD

Observability

Security/guardrails

Leadership

Education

Bachelor's degree in related field

Tools

Azure OpenAI

Bedrock

Vertex AI

Job description

Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life-changing technologies spans the spectrum of healthcare, with leading businesses and products in diagnostics, medical devices, nutritionals and branded generic medicines. Our 122,000 colleagues serve people in more than 160 countries.

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.

The base pay for this position is $149,300.00 – $298,700.00. In specific locations, the pay range may vary from the range posted.