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.
JOB FAMILY
IT Services & Solutions Delivery
DIVISION
BTS Business Technology Services
LOCATION
United States > Abbott Park : AP06C
ADDITIONAL LOCATIONS
WORK SHIFT
Standard
TRAVEL
Yes, 10 % of the Time
MEDICAL SURVEILLANCE
Not Applicable
SIGNIFICANT WORK ACTIVITIES
Continuous sitting for prolonged periods (more than 2 consecutive hours in an 8 hour day)
Abbott is an Equal Opportunity Employer of Minorities/Women/Individuals with Disabilities/Protected Veterans.
EEO is the Law link - English: http://webstorage.abbott.com/common/External/EEO_English.pdf
EEO is the Law link - Espanol: http://webstorage.abbott.com/common/External/EEO_Spanish.pdf
