Senior AI Engineer
gehealthcare
Job Description
- Design, build, and deploy agentic AI systems that support autonomous reasoning, planning, tool use, multi-step execution, and human-in-the-loop workflows for Imaging360 use cases.
- Develop scalable AI/ML and GenAI solutions across the full lifecycle, including data ingestion, feature engineering, model experimentation, evaluation, deployment, monitoring, and continuous improvement.
- Architect and implement Retrieval-Augmented Generation solutions that combine structured and unstructured healthcare, product, operational, and workflow data with strong grounding, relevance, and traceability.
- Design knowledge graph and GraphRAG solutions that model relationships across imaging assets, clinical entities, devices, and workflows, combining graph traversal with vector retrieval for multi-hop reasoning and explainable grounding.
- Engineer evaluation and testing harnesses for agentic systems, including automated eval pipelines, golden datasets, LLM-as-judge scoring, regression suites for prompts and agents, simulation-based agent testing, and red-teaming for adversarial robustness.
- Optimize token usage, latency, and inference cost through context engineering, prompt compression, prompt and semantic caching, model routing between frontier and small language models, batching, and structured output constraints.
- Build platform-agnostic AI services using appropriate cloud-native, open-source, or enterprise AI capabilities while avoiding dependency on a single AI vendor or model provider.
- Integrate LLMs, embedding models, orchestration frameworks, vector stores, APIs, and backend services into secure, reliable, and maintainable production applications.
- Create rapid proofs of concept for emerging AI, GenAI, and agentic patterns; harden successful prototypes into reusable production components and engineering patterns.
- Collaborate with product managers, architects, UX, data engineering, cybersecurity, quality, regulatory, cloud operations, and scrum teams to translate Imaging360 business needs into responsible AI solutions.
- Define and implement AI evaluation methods for accuracy, relevance, grounding, robustness, latency, cost, safety, fairness, explainability, and operational reliability.
- Drive engineering excellence through clean architecture, API design, automated testing, CI/CD, documentation, code reviews, design reviews, and production readiness practices.
- Ensure AI solutions meet healthcare-grade expectations for privacy, security, auditability, data governance, compliance, and responsible AI adoption.