Senior Data Engineer — Retrieval & RAG Platform

Solvd, Inc. is hiring a senior data engineer to own the retrieval workstream for a client’s Agent Assist platform. The role builds and operates a secure, versioned RAG retrieval API, its underlying Bedrock and OpenSearch infrastructure, and quality monitoring, then supports the pilot and handover.

Responsibilities:

• Stand up the Bedrock Knowledge Base and OpenSearch Serverless vector collection in the client’s account from infrastructure code, with the embedding model, chunking strategy, and metadata fields the architect specified
• Implement hybrid retrieval — vector plus keyword — with metadata filtering by taxonomy, source, status, and review date; tune top-k, score thresholds, and reranking against the golden question suite
• Build the retrieval API on API Gateway and Lambda per the Integration Blueprint — versioned paths, authentication for the Agent Assist provider, per-client rate limits and usage metrics, structured error responses, and a citation payload with source URL, title, section, and last modified date
• Enforce entitlement at query time by filtering on permission metadata the ingestion pipeline captures — no restricted passage is ever returned to an unentitled caller
• Provide the Bedrock retrieve-and-generate reference endpoint used for pilot validation in weeks 7–9
• Build the quality dashboard — relevance, grounding, and citation scores trended per run; permission test results; freshness lag per source; API latency and error rate; OpenSearch capacity
• Support the contact center pilot in weeks 7–9 — capture retrieval misses, tune, and re-run the suite before each change ships
• Write the retrieval and API runbooks including model change, re-embedding, and index rebuild procedures with cost implications
• Pair with client platform owners in weeks 9–10 and hand over on the transition readiness checklist

Requirements:

• 4+ years as a software or ML engineer, with at least one RAG or enterprise search system delivered to production and measured with an evaluation suite — not just demos
• Hands-on with Amazon Bedrock — Knowledge Bases, Retrieve and RetrieveAndGenerate APIs, embedding models — and Amazon OpenSearch Serverless vector search including hybrid queries and metadata filters
• Python, with experience shipping Lambda-based APIs behind API Gateway including auth, versioning, and throttling
• Infrastructure-as-code with Terraform or AWS CDK and CI for deployment into a client-owned account
• Practical understanding of chunking, embedding model selection, reranking, and the trade-offs between relevance, latency, and cost
• Experience building operational dashboards in CloudWatch or an equivalent tool
• AWS Certified Generative AI Developer Professional or AWS Certified Machine Learning Engineer Associate
• Experience with RAG evaluation frameworks and metrics — faithfulness, context precision and recall, citation accuracy
• Experience integrating with a contact center Agent Assist product as an API provider
• Familiarity with Bedrock Guardrails and permission-aware retrieval patterns over Microsoft 365 content