2 of 2 Data Engineering Jobs in Wetherby

were building the AI platform of choice for modern dealmakers. Were a fast-growing start-up where ideas move quickly from concept to product. Our technology spans backend, frontend, data, and AI, giving our team real scope to shape the future of the product and the company itself. If youre excited by solving complex problems end-to-end, working … in the companys journey towards achieving its business milestones. Responsibilities (including but not limited to): Backend APIs (Python/FastAPI): Build reliable, secure services that power AI features and data retrieval at scale. RAG & vector search: Design, implement and iterate retrieval pipelines (chunking, embeddings, hybrid search, ranking, feedback loops). Own pgvector/Vector DB schemas, latency, relevance and … cost. LLM integration: Integrate OpenAI/Bedrock models, prompt/response orchestration, tool use, guardrails, and evaluation. Data pipelines: Ingest and transform structured/unstructured data; design efficient schemas (Postgres/NoSQL) to support retrieval and analytics. Frontend (React/Next.js): Ship fast, accessible UIs that expose AI features clearly (search, filters, explanations, citations). Architecture: Evolve a More ❯

building the AI platform of choice for modern dealmakers. We’re a fast-growing start-up where ideas move quickly from concept to product. Our technology spans backend, frontend, data, and AI, giving our team real scope to shape the future of the product and the company itself. If you’re excited by solving complex problems end-to-end … the company’s journey towards achieving its business milestones. Responsibilities (including but not limited to): Backend APIs (Python/FastAPI): Build reliable, secure services that power AI features and data retrieval at scale. RAG & vector search: Design, implement and iterate retrieval pipelines (chunking, embeddings, hybrid search, ranking, feedback loops). Own pgvector/Vector DB schemas, latency, relevance and … cost. LLM integration: Integrate OpenAI/Bedrock models, prompt/response orchestration, tool use, guardrails, and evaluation. Data pipelines: Ingest and transform structured/unstructured data; design efficient schemas (Postgres/NoSQL) to support retrieval and analytics. Frontend (React/Next.js): Ship fast, accessible UIs that expose AI features clearly (search, filters, explanations, citations). Architecture: Evolve a More ❯