Agentic Ai Engineer Jobs

About The Position

Requirements

• 10–15+ years of software engineering experience, including substantial delivery of distributed, API-driven, or workflow-intensive production systems and technical leadership.
• Hands-on experience designing, coding, testing, deploying, and operating agentic or LLM-based applications in production; the role remains an active technical contributor.
• Strong understanding of agent architecture, tool calling, durable state, multi-agent coordination, context engineering, retrieval, evaluation, observability, and safety controls.
• Strong software engineering ability in Python and at least one of TypeScript, Go, or Java, including APIs, distributed systems, CI/CD, testing, code review, and secure SDLC.
• Experience defining measurable quality, safety, reliability, latency, and cost indicators and using evaluation results to improve systems through iterative releases.
• Bachelor's degree in a related field or equivalent professional experience.
• Demonstrated ability to lead engineers, make sound technical decisions under uncertainty, and communicate effectively with technical and non-technical stakeholders.

Nice To Haves

• Experience with agent frameworks or durable workflow systems such as LangGraph, Temporal, or comparable technologies, and model-provider APIs.
• Experience in a highly regulated environment such as health tech, med-tech, or life sciences, including GxP, HIPAA, validation, auditability, and change-control constraints.
• Experience building secure tool-use systems, prompt-injection defenses, human-approval workflows, responsible-AI controls, and privacy-preserving agent experiences.
• Experience shipping reusable agent patterns, evaluation tooling, SDKs, or paved-road capabilities for multiple product or engineering teams.

Responsibilities

• Design, implement, and operate production agentic systems, including supervisor, planner/executor, and multi-agent patterns with explicit boundaries, failure handling, and human oversight.
• Build durable, resumable workflows with persistent state, checkpoints, replay, idempotency, compensation, and human-in-the-loop controls.
• Engineer tool and function integrations with Abbott enterprise systems, including tool contracts, schemas, authorization, retries, timeouts, compensation, and failure containment.
• Develop context-engineering strategies for retrieval, chunking and indexing, memory hierarchies, context-window budgeting, caching, structured outputs, and grounding.
• Create evaluation systems using curated datasets, task-success and safety metrics, LLM-as-judge and programmatic evaluators, CI regression gates, and online, A/B, and shadow evaluation.
• Instrument agent applications through model and agent registries and tracing services to make prompts, tool calls, intermediate steps, quality, cost, and latency observable and auditable.
• Engineer production reliability through streaming, concurrency, latency and token-cost optimization, rate limits, graceful degradation, secure deployment, SLOs, on-call, incident response, and rollback of agent regressions.
• Write and review substantial production code in Python and at least one of TypeScript, Go, or Java; establish testing, code review, API design, and secure software engineering practices.
• Lead technical discovery and delivery with AI Product Managers, Platform Engineering, Applied Data Science, security, and business stakeholders; communicate feasibility, risks, and delivery commitments.
• Hire, coach, and develop approximately 3–8 agentic engineers; set goals, allocate work, review performance, and create reusable patterns that feed back into the platform paved road.