Solvd Senior Data Engineer at Solvd responsible for building the retrieval API and vector search infrastructure. Own the end-to-end retrieval workstream including entitlement filtering and quality dashboard.
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.
Qualification
4+ years as a software or ML engineerPythonPractical understanding of chunkingWhen you join Solvd
Required
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.
Preferred
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.
When you join Solvd, you'll...
Shape real-world AI-driven projects across key industries, working with clients from startup innovation to enterprise transformation.
Be part of a global team with equal opportunities for collaboration across continents and cultures.
Thrive in an inclusive environment that prioritizes continuous learning, innovation, and ethical AI standards.
