Principal AI Platform Engineer

Abbott abbott.com Open · verified Oct 30, 2026

Abbott Park Full-time Senior ML Platform & Ops

At a glance

Senior ML Platform & Ops role at Abbott. Abbott Park · full-time · $149,300–$298,700 base.

SalaryStated by Abbott

$149,300 – $298,700

Base salary for this role, in USD per year, as published in the posting.

RoleML Platform & Ops

SenioritySenior

LocationAbbott Park

EmploymentFull-time

Base pay$149,300–$298,700

PostedOct 12, 2026 · 2w ago

Role brief

aimlroles summary, based on the employer's posting.

What you'll do

Set the AI platform’s architecture, then build shared services, SDKs and templates that make deployment easier
Create model-routing and GenAI runtime services for retrieval, memory, search, sandboxed execution and tool connections
Manage registries for models, agents, prompts and tools, including version history, approvals, retirement and audit records
Add tracing and quality, drift, speed and cost monitoring across prompts, model use, tools and agent activity
Set reliability and security practices, write production code, lead incidents, and grow a hands-on platform team

What you bring

At least ten years in software, platform, distributed-systems or infrastructure engineering, with substantial technical leadership
Production experience operating enterprise AI platforms or comparable developer, data or ML platforms
Strong Python skills plus TypeScript, Go or Java, and experience with APIs, distributed systems, testing and secure development
A related bachelor's degree or equivalent professional experience

Who this fits

You’ll suit a senior technical leader who wants to stay hands-on while hiring and developing an engineering team. The role is based in Abbott Park and includes travel about ten percent of the time.

From the employer

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.

JOB DESCRIPTION:

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.