Principal AI Platform Engineer
Abbott Laboratories
Abbot, IL, United States
3 days ago
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Role details
Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
10 years minimum
Working hours
Regular working hours
Job source
Tech stack
Java (Programming Language)
Application Programming Interfaces (APIs)
Artificial Intelligence
Audit Trail
Microsoft Azure
Cloud Foundry
Code Review
Continuous Integration
Distributed Systems
Python (Programming Language)
Key Management
Network Control
+23 more
Systems Development Life Cycle
Reliability Engineering
OpenAI
Cloud Services
Azure Machine Learning
Software Engineering
TypeScript
Management of Software Versions
AI Infrastructure
Retrieval-Augmented Generation
Prompt Engineering
Langfuse β LLM Observability and Analytics Platform
Rate Limiting
Build Management
AI Platforms
Kubernetes
Infrastructure Automation Frameworks
Amazon Bedrock
Production Code
LiteLLM
Artificial Intelligence Governance
Dynatrace
GXP
Job description
- 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.
Requirements
- 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.
- Bachelorβs 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., * 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.
Benefits & conditions
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. #J-18808-Ljbffr
About the company
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.
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