Staff AI Platform Engineer

At Abbott, you can do work that matters, grow, and learn, care for yourself and your family, be your true self, and live a full life. You’ll also have access to:

• Career development with an international company where you can grow the career you dream of.
• Employees can qualify for free medical coverage in our Health Investment Plan (HIP) PPO medical plan in the next calendar year.
• An excellent retirement savings plan with a high employer contribution
• Tuition reimbursement, the Freedom 2 Save student debt program, and FreeU education benefit - an affordable and convenient path to getting a bachelor’s degree.
• A company recognized as a great place to work in dozens of countries worldwide and named one of the most admired companies in the world by Fortune.
• A company that is recognized as one of the best big companies to work for as well as the best place to work for diversity, working mothers, female executives, and scientists.

A senior individual-contributor engineering role that drives architecture and delivery for significant portions of Abbott's enterprise AI Platform. This role sets technical direction for one or more platform capability areas, partners closely with the Tech Lead/Architect on reference architecture decisions, and is a primary technical escalation point for consuming teams. Expected to operate with substantial autonomy and to raise the technical bar for the team.

• →Lead the design of major platform capabilities (e.g., multi-provider model gateway, GenAI runtime, agent/model registries, evaluation and tracing pipelines) with minimal oversight, in partnership with the Tech Lead/Architect.
• →Make and document architecture decisions and tradeoffs for the platform's control plane, paved-road SDKs, and developer experience, weighing scalability, security, cost, and operability.
• →Drive platform observability and evaluation strategy, including distributed tracing, quality/drift/latency/cost signals, and standards other engineers build against.
• →Lead implementation of security, guardrails, and responsible-AI controls (PII/PHI handling, prompt-injection defenses, identity-on-behalf-of, policy enforcement) across platform services.
• →Own platform reliability engineering: define SLOs, lead incident response for high-severity issues, drive root-cause analysis, and improve resilience.
• →Set engineering standards through architecture reviews, design docs, and code reviews; identify and resolve systemic technical risk before it blocks delivery.
• →Make build-versus-buy and vendor recommendations with cost/tradeoff analysis, in partnership with the Tech Lead/Architect and Product Manager.
• →Mentor and technically develop mid-level and junior engineers; act as a technical lead on cross-team initiatives without formal people-management responsibility.
• →Partner with the Product Manager, AI Platform to sequence roadmap, and directly engage senior stakeholders across AI/Agentic Engineering, Applied Data Science, and security on feasibility and design.

• 8–10 years of software engineering, platform engineering, distributed systems, or infrastructure experience, with a track record of owning significant technical scope.
• Hands-on experience designing and operating enterprise AI/GenAI platforms or comparable developer, data, or ML platforms in production at scale.
• Strong software engineering ability in Python and at least one of TypeScript, Go, or Java, including distributed systems, API design, testing, and secure SDLC.
• Deep technical understanding of at least two of: model gateways, GenAI runtimes, RAG, agent/model registries, evaluation, observability/tracing, guardrails, or platform governance.
• Experience with containers and orchestration (e.g., Kubernetes), infrastructure as code, CI/CD, cloud services, and production SLO/on-call practices, including leading incident response.
• Bachelor's degree in a related field or equivalent professional experience.
• Demonstrated ability to drive technical decisions with significant autonomy, influence engineers outside a direct reporting line, and communicate clearly with technical leadership.

• 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, and change-control constraints.
• Experience implementing security, responsible-AI, privacy, policy, or cost/FinOps controls for shared AI infrastructure.
• Experience with open standards and tooling such as MCP, OpenTelemetry, Kubernetes, and cloud-native platform engineering practices; prior informal technical leadership or mentoring track record.