Jobs / United States / Abbott Laboratories
Principal AI Platform Engineer
Abbott Laboratories · 🇺🇸 United States - Illinois - Abbott Park
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 73 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 28 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Abbott Laboratories paid sponsored hires in similar roles1 certified filing for “SENIOR WAN ENGINEER” (Network and Computer Systems Administrators) in IL: $122k–$122k, median $122k. Most were filed at wage level IV (100%) — 4 lottery entries, ≈61% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence; it is not legal advice and never a guarantee — the employer and the immigration authority decide. Sign in to factor in where you can already work.
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Sponsor Radar — Abbott Laboratories
The US Department of Labor certified 73 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 28 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Abbott Laboratories →
About the role
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 orc