Senior Data Engineer — Retrieval & RAG Platform

Role overview Own the retrieval workstream for an enterprise knowledge and agent-assistance solution, from cloud infrastructure and search design through API delivery, evaluation, and operational handover. The core outcome is a versioned service that returns relevant, cited passages while enforcing caller permissions and meeting agreed latency and cost expectations.

Responsibilities - Provision a managed knowledge base and vector search collection in the client’s AWS account using infrastructure code, with the specified embeddings, chunking approach, and metadata. - Build hybrid vector and keyword search, apply metadata filters, and tune ranking against an evaluation question set. - Deliver a versioned Python retrieval API using Lambda and API Gateway, including authentication, rate limits, usage metrics, structured errors, and citations with source details. - Apply permission metadata at query time so callers only receive content they are authorized to access. - Create a reference retrieve-and-generate endpoint and support pilot testing by investigating retrieval misses and refining results. - Build operational quality dashboards and write runbooks for model changes, re-embedding, index rebuilds, costs, and service handover.

Requirements - At least four years of software or machine-learning engineering experience, including a production RAG or enterprise search system evaluated with a repeatable test suite. - Practical experience with Amazon Bedrock Knowledge Bases and retrieval APIs, embedding models, and OpenSearch Serverless vector search, including hybrid queries and metadata filters. - Strong Python skills and experience deploying Lambda APIs behind API Gateway with authentication, versioning, and throttling. - Infrastructure-as-code experience with Terraform or AWS CDK, plus CI for deployments into a client-owned AWS account. - Understanding of chunking, embedding selection, reranking, and the trade-offs among relevance, latency, and cost. - Experience creating operational dashboards in CloudWatch or a comparable tool.

Nice to have - Relevant AWS certification in generative AI development or machine learning engineering. - Familiarity with RAG evaluation measures such as faithfulness, context precision and recall, and citation accuracy. - Experience providing APIs to contact-center agent-assistance products. - Knowledge of Bedrock Guardrails and permission-aware retrieval for Microsoft 365 content.

Benefits and work setup Work with an international team on applied AI projects, with an emphasis on ongoing learning, collaboration, and responsible AI practices.