Note: The job is a remote job and is open to candidates in USA. Clearsense is hiring a Senior AI Engineer for its Data Gateway product team, which defines how healthcare data is acquired, curated, and delivered. The role builds reusable semantic knowledge bases and internal AI agent tools that help engineers discover, map, validate, and document data, primarily on AWS.
Responsibilities
- Design and build the knowledge bases the Data Gateway team relies on: source-system profiles, data dictionaries, extraction and curation patterns, runbooks, and curated document collections
- Own the retrieval pipeline end to end: parsing (including OCR'd multi-page PDFs), chunking, metadata, embeddings, hybrid and structured retrieval, and reranking
- Pick the right store for each corpus, such as Amazon Bedrock Knowledge Bases (managed or custom), Amazon S3 Vectors, Amazon OpenSearch Serverless, Aurora PostgreSQL with pgvector, or graph retrieval on Amazon Neptune Analytics, and document the trade-offs
- Keep knowledge current with automated sync, versioning, lineage back to source, and retirement of stale content
- Build agents and tools that help engineers profile sources, map fields, generate and review transformation code, triage validation failures, and draft documentation
- Build on Amazon Bedrock AgentCore (Runtime, Gateway, Memory, Identity, Policy, Observability, Evaluations), and expose internal systems to agents as MCP tools through AgentCore Gateway
- Keep a human in the loop where it matters: approval steps, audit trails, and clear limits on what an agent can change
- Define evaluation for every knowledge base and agent: golden datasets, retrieval and answer-quality metrics, and regression checks in CI/CD
- Apply security by default for PHI: least-privilege IAM, encryption, network isolation, Amazon Bedrock Guardrails, and logging that meets HIPAA and
Skills
- 5+ years in software, data, or ML engineering, including 2+ years building production LLM applications (RAG, agents, or both)
- Hands-on experience with Amazon Bedrock: model invocation, Knowledge Bases, Agents or AgentCore, and Guardrails
- Built and tuned retrieval systems end to end: chunking, embeddings, vector and hybrid search, reranking, and evaluation
- Strong Python and solid SQL
- AWS depth beyond AI services: IAM, S3, Lambda, Step Functions, ECS/Fargate, VPC networking, and CloudWatch, plus infrastructure as code (CDK or Terraform) and CI/CD
- Evaluation discipline: you measure retrieval and answer quality and ship against the numbers
- Experience handling sensitive or regulated data with the right controls
- Clear communicator who documents well and can teach other engineers
- Healthcare data experience: HL7, FHIR, or CCD, and EHRs such as Epic, Oracle Health (Cerner), or MEDITECH
- Elasticsearch experience, especially semantic or hybrid search over large document indices
- Document AI: OCR pipelines, Amazon Textract, and layout-aware parsing of scanned clinical PDFs
- Knowledge graphs or GraphRAG (Amazon Neptune or similar)
- Building MCP servers and tools, and agent frameworks such as Strands Agents or LangGraph
- Work in HIPAA or HITRUST environments
- Data engineering background (Airflow, Spark, NiFi) to work alongside Data Gateway Engineers on pipelines
Benefits
- Remote work
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