Job Description Summary

Job Description

Roles and Responsibilities:

• Own the end-to-end GenAI strategy within customer environmentsfrom discovery and problem decomposition to production deployment and adoption.

• Define solution architectures across RAG agentic AI knowledge bases and multi-turn conversational systems with memory.

• Establish robust evaluation frameworks (e.g. groundedness hallucination rate factuality latency cost and safety) and drive iterative improvement.

• Design and deploy large-scale LLM workflows RAG systems (vector/hybrid reranking) Graph RAG (entity/relation extraction query planning) and autonomous/agentic systems (task decomposition tool use planning/feedback loops).

• Implement conversation memory (short-term context buffers long-term vector/graph stores entity memories) to support multi-turn experiences.

• Build conversational chatbots and search strategies (BM25 dense retrieval hybrid retrieval query rewriting/expansion) for high-precision answers.

• Apply chunking strategies (semantic/hierarchical overlap/sliding window summarization-based chunking) to optimize retrieval quality and cost.

• Leverage GEHC STO AI Fabric components to build robust data pipelines define/align ontologies and integrate ML/LLM models into operational tools and workflows.

• Develop and register components (transforms ontology services pipelines) ensuring traceability observability and reusability within the AI Fabric.

• Contribute field learnings patterns and edge cases back to the AI Fabric product teams to mature the platform and accelerate customer outcomes.

• Work closely with customer executives SMEs and end users to understand mission-critical needs and translate them into technical solutions and delivery plans.

• Communicate tradeoffs (quality latency cost safety) and guide stakeholders to data-driven decisions.

• Implement CI/CD containerization (Docker) model packaging deployment and monitoring across environments (dev/stage/prod).

• Build LLMOps observability: tracing prompt/version management cost/latency dashboards eval pipelines A/B testing safety guardrails and human-in-the-loop review where required.

• Operate within AWS (SageMaker for training/inference Bedrock for managed foundation models); manage infrastructure-as-code and environment hardening.

• Build scalable pipelines using Databricks Spark/PySpark and SQL to ingest clean and feature/signal engineer multi-modal data.

• Architect and manage knowledge bases (document stores vector DBs knowledge graphs) and retrieval services for production traffic.

• Bias for shipping high-quality maintainable software over academic benchmarks.

• Write clean testable code; enforce code quality documentation and reusability.

• Mentor peers; elevate engineering best practices for GenAI at scale.

Qualifications:

• Bachelors Degree in Computer Science or STEM Majors (Science Technology Engineering and Math) with a minimum of 5 years in AI/ML engineering data science or applied research; 1.5 years building production LLM/GenAI systems.

• Excellent coding proficiency in Python and either Java or TypeScript; solid software engineering fundamentals (testing modularity design patterns).

• Deep understanding of LLMs and agentic AI (planning tool use function calling multi-agent collaboration).

• Hands-on with RAG (vector/hybrid retrieval reranking query planning) Graph RAG (entities/relations graph queries) knowledge bases and chunking strategies.

• NLP expertise (retrieval generation summarization information extraction); working knowledge of computer vision and GANs for augmentation or synthesis.

• Proven experience with conversational AI (multi-turn with memory grounding disambiguation) and search strategies (BM25 hybrid semantic reranking).

• Databricks (Delta Unity Catalog Jobs/Workflows) Spark/PySpark and SQL at scale.

• MLOps LLMOps Docker Kubernetes CI/CD and AWS (SageMaker Bedrock) for model training evaluation and deployment.

• Practical experience with LangChain LangGraph CrewAI n8n (or similar orchestrators) and using OpenAI/Anthropic models in production.

• Good customer-facing communication skills and the ability to operate on-site with customers when needed.

• Experience in regulated domains (healthcare life sciences) and ontology-driven systems.

• Knowledge graphs (e.g. Neo4j) vector stores and hybrid retrieval with cross-encoder reranking.

• Prompt engineering and LLM evaluation frameworks; safety guardrails (PII/PHI redaction toxicity filters).

• Familiarity with Kubernetes (EKS/ECS) feature stores experiment tracking and model registries.

• Performance optimization for long-context models batching streaming and cost/latency tuning.

Inclusion and Diversity

GE Healthcare is an Equal Opportunity Employer where inclusion matters. Employment decisions are made without regard to race color religion national or ethnic origin sex sexual orientation gender identity or expression age disability protected veteran status or other characteristics protected by law.

We expect all employees to live and breathe our behaviors: to act with humility and build trust; lead with transparency; deliver with focus and drive ownership always with unyielding integrity.

Our total rewards are designed to unlock your ambition by giving you the boost and flexibility you need to turn your ideas into world-changing realities. Our salary and benefits are everything youd expect from an organization with global strength and scale and youll be surrounded by career opportunities in a culture that fosters care collaboration and support.

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