Staff AI/Agentic Engineer

Abbott; Illinois; On-site; USD 130,000 - 190,000; Full time

Benefits offered by this job

Free medical coverage

Excellent retirement plan

Tuition reimbursement

Job summary

Abbott in the United States seeks a senior software engineer to set technical direction for production agentic systems. You will design complex multi-agent solutions and reusable patterns, partnering with the Tech Lead on architecture and serving as a primary escalation point across the AI/Agentic Engineering team.

You will drive durable workflow design, context-engineering, retrieval strategies, and evaluation; own production reliability at scale, while mentoring engineers and communicating

Qualifications

• 7-10 years of software engineering experience with distributed, API-driven production systems.
• Hands-on experience designing, coding, testing, deploying, and operating agentic or LLM-based applications in production at scale.
• Deep understanding of agent architecture, tool calling, context engineering, retrieval, observability, and safety controls.
• Strong Python and at least one of TypeScript, Go, or Java, including distributed systems, API design, testing, and secure SDLC.

Responsibilities

• Lead design and implementation of complex multi-agent systems with supervisor, planner/executor, and coordination patterns.
• Set standards for durable workflow design (state, checkpoints, replay, idempotency, compensation, human-in-the-loop).
• Drive context-engineering and retrieval strategies across multiple workflows (chunking/indexing, memory hierarchies, grounding).
• Lead evaluation strategy: design evaluation systems, curate datasets, define task-success metrics, and establish regression gates in CI/online evaluation.
• Own production reliability for high-complexity agentic systems: latency/cost optimization, rate-limiting, graceful degradation, incident leadership.
• Set engineering standards via architecture reviews, design docs, and code reviews; address systemic risk.
• Create reusable agent patterns and tooling with AI Platform Engineering.
• Lead technical discovery with AI Product Managers and stakeholders; communicate feasibility and delivery commitments.
• Mentor mid-level and junior engineers; act as technical lead on cross-team initiatives.

Skills

Distributed systems

Python

API design

Multi-agent coordination

Documentation

Cloud/DevOps

Education

Bachelor's degree in a related field

Tools

LangGraph

Temporal

Job description

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.

Working at Abbott

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.

The Opportunity

A senior individual-contributor engineering role that sets technical direction for production agentic systems at Abbott. This role leads the design of complex multi-agent solutions and reusable engineering patterns, partners closely with the Tech Lead/Architect on architecture decisions and is a primary technical escalation point across the AI/Agentic Engineering team . Expected to operate with substantial autonomy and to raise the technical bar for the team.

What You'll Do

• Lead design and implementation of complex multi-agent systems, including supervisor, planner/executor, and multi-agent coordination patterns with well-defined boundaries and failure handling.
• Set standards for durable workflow design (state, checkpoints, replay, idempotency, compensation, human-in-the-loop) used across the AI/Agentic Engineering team.
• Drive context-engineering and retrieval strategy across multiple workflows: chunking/indexing, memory hierarchies, grounding, and context-window budgeting.
• Lead evaluation strategy: design evaluation systems, curate datasets, define task-success and safety metrics, and establish regression gates in CI and online/shadow evaluation.
• Own production reliability for high-complexity or high-risk agentic systems: latency/cost optimization at scale, rate-limit strategy, graceful degradation, and incident response leadership.
• Set engineering standards through architecture reviews, design docs, and code reviews; identify and resolve systemic technical risk in agent delivery.
• Create reusable agent patterns, SDKs, and evaluation tooling that feed back into the platform paved road, in partnership with AI Platform Engineering.
• Lead technical discovery with AI Product Managers, Platform Engineering, Applied Data Science, and business stakeholders; communicate feasibility, risk, and delivery commitments directly to senior stakeholders.
• Mentor and technically develop mid-level and junior engineers; act as technical lead on cross-team initiatives without formal people-management responsibility.

Education and Experience You'll Bring

Required Qualifications

• 7-10 years of software engineering experience, with substantial delivery of distributed, API-driven, or workflow-intensive production systems.
• Hands‑on experience designing, coding, testing, deploying, and operating agentic or LLM-based applications in production at scale; expected to remain an active technical contributor.
• Deep 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 distributed systems, API design, testing, and secure SDLC.
• Experience defining measurable quality, safety, reliability, latency, and cost indicators and using evaluation results to drive systemic improvement across multiple workflows.
• 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 effectively with technical leadership.

Preferred Qualifications

• Experience with agent frameworks or durable workflow systems (e.g., LangGraph, Temporal, or comparable technologies) and model-provider APIs at scale.
• Experience in a highly regulated environment such as health tech, med-tech, or life sciences, including GxP, HIPAA, validation, and change-control constraints.
• Experience building secure tool-use systems, prompt-injection defenses, human-approval workflows, and responsible-AI controls for prod