Sr AI Engineer

GE HEALTHCARE•Bellevue, WA

8d•$196,000 - $294,000•Onsite

About The Position

As an 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.

Requirements

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.
Legal authorization to work in the U.S. is required.
Must be willing to travel to attend meetings, workshops, conferences & etc.
Must be willing to work out of an office located in the greater Seattle Area.

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.