Sr AI Engineer

GE Healthcare GE Healthcare · Healthcare · Bellevue, WA +2 · Digital Technology / IT

This role focuses on designing, building, and deploying agentic AI systems for healthcare use cases. It involves orchestrating LLMs, tools, data sources, and workflows to create intelligent agents that can reason, plan, act, and learn in complex environments. The role bridges AI research with scalable systems for regulated healthcare settings.

What you'd actually do

  1. 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.
  2. 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.
  3. Orchestrating AI agents across data modalities including medical images, electronic medical records, waveforms, and clinical reports.
  4. Defining evaluation strategies for agent behavior, including task success, robustness, safety, and alignment—beyond traditional model accuracy metrics.
  5. Developing production‑ready systems for agent deployment, monitoring, observability, and lifecycle management, including guardrails, auditing, and human‑in‑the‑loop controls.

Skills

Required

  • Graduate degree in Computer Science or a related field, with two or more years of industry experience.
  • Strong experience in software engineering for AI systems, including designing distributed, stateful, or event‑driven services.
  • Hands‑on experience with LLMs and agent frameworks, such as tool calling, function execution, retrieval‑augmented generation (RAG), memory, and planning patterns.
  • Proficiency in one or more general‑purpose programming languages (e.g. Python, Java, C/C++).
  • Experience working with large‑scale data and production platforms (e.g. Spark, Hadoop, cloud‑native services).
  • Practical experience deploying and operating AI systems in production, including monitoring, logging, and failure handling.

Nice to have

  • 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).

What the JD emphasized

  • agentic AI architectures
  • multi-step reasoning and decision-making agents
  • agent deployment, monitoring, observability, and lifecycle management
  • regulated healthcare settings
  • autonomous or semi-autonomous agents in real-world, noisy, or high-stakes environments

Other signals

  • Designing and developing agentic AI architectures
  • Building multi-step reasoning and decision-making agents
  • Orchestrating AI agents across data modalities
  • Defining evaluation strategies for agent behavior
  • Developing production-ready systems for agent deployment, monitoring, observability, and lifecycle management