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.

• 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 No
  
  SQL 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.

• 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 / Type Script.
• Strong grasp of JavaScript/Type Script fundamentals, async patterns, and event‑driven architecture.
• Solid experience designing and scaling RESTful and/or GraphQL APIs.
• Proven experience with relational and No
  
  SQL 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.

• 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 Lang Chain, Llama Index, 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.