Sr. Staff AI Engineer

GE HealthCare•Bellevue, WA

1d•Onsite

About The Position

As a Sr. Staff AI Engineer, you will play a critical role in designing, building, and deploying agentic AI systems that reason, plan, act, and learn in complex real-world environments. You will develop end-to-end intelligent agents that orchestrate large language models (LLMs), tools, data sources, and workflows to deliver reliable, production-grade automation for clinical and operational use cases. You will work at the intersection of AI research, software engineering, and product delivery—bridging AI capabilities with scalable systems that can operate autonomously, collaborate with humans, and adapt over time within regulated healthcare settings. GE Healthcare is a leading global medical technology and digital solutions innovator. Our mission is to improve lives in the moments that matter. Join an organisation where every voice makes a difference, and every difference builds a healthier world.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, with 8+ years of industry experience building complex software systems.
Strong background in software engineering for AI-driven systems, including distributed, stateful, event-driven, or service-oriented architectures.
Hands-on experience designing and implementing LLM-based and agentic systems, including tool calling, function execution, retrieval-augmented generation (RAG), memory management, and planning or orchestration patterns.
Proficiency in one or more general-purpose programming languages (e.g., Python, Java, C/C++), with a track record of writing production-quality code.
Experience working with large-scale data platforms and cloud-native systems, including distributed compute, storage, and service infrastructure.
Proven experience deploying, operating, and supporting mission-critical AI systems in production, including monitoring, logging, observability, failure handling, and lifecycle management.

Nice To Haves

Experience building autonomous or semi-autonomous agents in real-world, noisy, or high-stakes environments.
Familiarity with AI safety, guardrails, interpretability, and human-in-the-loop system design.
Experience handling real-world medical or patient data and working within regulated environments.
Background in machine learning or deep learning (e.g. PyTorch, TensorFlow).

Responsibilities

Designing and developing agentic AI architectures that combine LLMs with planning, tool use, memory, and feedback loops to automate and augment clinical and operational workflows.
Building multi-step reasoning and decision-making agents that can decompose tasks, select tools, invoke external systems (e.g. databases, APIs, services), and adapt based on outcomes.
Orchestrating AI agents across data modalities including medical images, electronic medical records, waveforms, and clinical reports.
Defining evaluation strategies for agent behavior, including task success, robustness, safety, and alignment—beyond traditional model accuracy metrics.
Developing production-ready systems for agent deployment, monitoring, observability, and lifecycle management, including guardrails, auditing, and human-in-the-loop controls.
Building scalable, service-oriented platforms and reusable components that enable multiple teams to compose, customize, and govern AI agents across products.
Prototyping and iterating quickly to validate agent behavior in realistic workflows, then hardening solutions for regulated, high-reliability environments.
Staying current with advances in agentic AI, LLM tooling, orchestration frameworks, and applied AI system design.