Senior Backend Engineer

We are looking for a Senior Backend Engineer who builds fast, reliable systems and treats AI as a first-class engineering tool, not a buzzword. You will design and ship production-grade backend services in Node.js, and integrate modern AI capabilities (LLMs, RAG, agents, embeddings) into real product workflows. This is a hands‑on role for someone who writes clean code, owns systems end‑to‑end, and uses AI to move faster without compromising quality. Responsibilities Design, build, and scale backend services and REST/GraphQL APIs using Node.js (Express / NestJS / Fastify). Integrate LLM-powered features into products, RAG pipelines, semantic search, chat/agent workflows, and structured AI outputs. Work with vector databases (pgvector, Pinecone, Weaviate, Qdrant) and embedding models for retrieval and recommendation. Architect data layers across PostgreSQL, Redis, and NoSQL stores, with a focus on performance and low latency. Build and consume APIs from AI providers (OpenAI, Anthropic, Bedrock, etc.) with proper cost, latency, and reliability controls. Use AI‑assisted development tools (Copilot, Claude, Cursor) to ship higher‑quality code faster. Write tests, set up CI/CD, and own services in production monitoring, logging, and incident response. Collaborate with product, frontend, and data teams to turn requirements into well‑architected systems. Requirements Must‑have items are required from day one. Required items are expected within your first month. Optional items are a strong plus. 4‑5 years of professional backend development experience, primarily in Node.js / TypeScript. Strong grasp of JavaScript/TypeScript fundamentals, async patterns, and event‑driven architecture. Solid experience designing and scaling RESTful and/or GraphQL APIs. Proven experience with relational and NoSQL databases (PostgreSQL, MongoDB) and caching with Redis. Hands‑on experience integrating at least one LLM/AI API into a real product or feature. Comfortable with Docker, Git, and CI/CD pipelines. Understanding of system design, scalability, and security best practices. AI‑Native Skills (Strongly Preferred) Practical experience building RAG pipelines or retrieval systems with vector databases. Familiarity with prompt engineering, function/tool calling, and structuring reliable LLM outputs. Experience with AI frameworks such as LangChain, LlamaIndex, or the Vercel AI SDK. Awareness of LLM cost optimisation, token management, streaming, and evaluation/guardrails. Daily use of AI coding assistants as part of a real engineering workflow. #J-18808-Ljbffr