Role Description
We’re seeking a Senior AI Engineer / Data Scientist to lead the design, deployment, and scaling of enterprise AI capabilities—specifically large language model (LLM) solutions, LLMOps practices, and the development of a healthcare ontology/knowledge graph to enhance a complex data environment.
Responsibilities
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Ontology & Knowledge Graph
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Design and maintain a healthcare ontology to normalize CMS data across claims, providers, and workflows.
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Build and manage knowledge graphs (RDF/OWL or property graph) to support semantic search, inference, and RAG augmentation.
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Develop graph data pipelines for ingestion, transformation, and entity resolution aligned with governance standards.
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Collaborate with SMEs to define controlled vocabularies and create reusable semantic APIs for analytics and AI.
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GenAI & LLMOps
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Architect and operationalize LLMs for production use cases including RAG, agentic workflows, and MCP tools.
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Build LLM evaluation and safety frameworks (prompt quality, grounding, hallucination detection, bias checks) with automated testing and human-in-the-loop reviews.
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Design cost- and latency-aware pipelines with observability for performance and reliability.
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Implement LLMOps best practices: prompt versioning, CI/CD for artifacts, rollout strategies, and A/B testing.
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Integrate vector databases and optimize chunking, embeddings, and retrieval for high-quality responses.
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Platform & System Architecture
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Support productionalization of AI/ML workflows with automated quality checks and lifecycle orchestration.
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Ensure data security, governance, and CMS compliance.
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Contribute to high-level system design for integrating new AI capabilities into a cloud-based analytics platform.
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Maintain documentation and acceptance criteria for system changes.
Qualifications
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Education/Experience: 10+ years with BS/BA; 8+ years with MS/MA; 5+ years with PhD in Computer Science, Data Science, or related field.
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Hands-on experience with LLMs and GenAI solutions, including prompt engineering, RAG architecture, and LLMOps practices.
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Proven experience in ontology design and knowledge graph development for complex data-driven systems.
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Experience with Databricks, Snowflake, and AWS Cloud Services.
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Proficiency in Python and SQL (Snowflake SQL).
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Experience with CI/CD workflows and automated deployments.
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Familiarity with Scaled Agile Framework (SAFe).
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Excellent communication skills and ability to work independently.
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US Citizenship required.
Preferred Qualifications
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Experience with Databricks E2 components (Unity Catalog, Feature Store).
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Knowledge of CMS systems and Medicare/Medicaid data.
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Familiarity with LLM/GenAI tooling (LangChain, LlamaIndex, Hugging Face, AWS Bedrock).
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Experience with vector databases and RAG orchestration.
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Knowledge graph tools: Neo4j, TigerGraph, AWS Neptune, RDF/OWL, SPARQL, Gremlin, Cypher, Protégé.
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Model lifecycle & governance: MLflow, Model Registry, feature stores, LLM safety testing.
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Observability & automation: GitHub Actions/Jenkins, Terraform, Docker/Kubernetes, Prometheus/Grafana.
Benefits
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Top-tier benefits package to invest in your physical, mental, and financial health and wellness.
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Opportunities to learn new skills, seize new challenges, and advance your career.
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Culture of inclusion and opportunity for all.