ROLE OVERVIEW
We are looking for a hands-on Senior AI Engineer to build production-grade AI systems using Generative AI, RAG, GraphRAG, knowledge graphs, vector search, LLM orchestration and computer vision. You will work on real-world AI products across healthcare, enterprise software and data platforms, taking solutions from experimentation to production.
The ideal candidate has strong software engineering fundamentals, hands-on experience with LLMs and RAG systems, and the ability to work across AI, backend, data and cloud technologies.
KEY RESPONSIBILITIES
AI & GenAI Engineering
- Build production-grade RAG and GraphRAG systems combining LLMs, vector search, knowledge graphs and structured data.
- Develop LLM workflows using LangChain, LangGraph, LlamaIndex or equivalent frameworks.
- Build AI agents, tool-calling workflows and stateful AI pipelines.
- Implement embeddings, semantic search, chunking, reranking and hybrid retrieval.
- Develop text-to-SQL, text-to-Cypher and natural-language query systems.
- Build LLM evaluation pipelines and optimise accuracy, latency and cost.
- Work with LLM APIs and batch processing using models such as OpenAI, Anthropic and other foundation models.
Knowledge Graphs & Data
- Design and work with Neo4j knowledge graphs, including graph modelling and Cypher queries.
- Build semantic enrichment pipelines including entity extraction, entity linking and relationship extraction.
- Develop data pipelines using Apache Airflow or equivalent orchestration tools.
- Work with PostgreSQL, Redis, Qdrant and other vector databases.
- Process and index large-scale enterprise datasets for AI applications
Platform & Data Engineering
- Architect multi-tenant SaaS platforms with strong data isolation across database, storage, API and orchestration layers.
- Build and maintain data pipelines with Apache Airflow, including ETL, DAG orchestration, data quality checks and real-time synchronisation.
- Design scalable APIs and services in Python (FastAPI) and Node.js / TypeScript, backed by PostgreSQL, Neo4j, Redis and vector stores.
- Deploy and operate workloads on AWS and GCP using serverless, containerised and event-driven patterns.
Security & Compliance
- Enforce HIPAA-grade controls: encryption with KMS, RBAC, JWT/OAuth authentication, audit logging and per-tenant key management.
- Translate regulatory and client compliance requirements into concrete architecture and engineering standards.
Leadership & Delivery (Nice to Have)
- Lead and mentor a team of 2 to 4 engineers through architecture reviews, code reviews, sprint planning and delivery milestones.
- Act as the primary technical point of contact for clients: gather requirements, propose solutions, report progress and manage expectations.
- Raise the bar on code quality, documentation, testing and engineering practices across the team.
REQUIRED SKILLS & EXPERIENCE
- 5+ years of software engineering experience with significant hands-on experience in AI/ML or GenAI.
- Strong Python skills and experience with FastAPI.
- Hands-on experience building RAG, LLM and AI agent applications.
- Experience with LangChain, LangGraph, LlamaIndex or equivalent.
- Strong understanding of embeddings, vector search and semantic retrieval.
- Experience with Qdrant, Pinecone, ChromaDB, pgvector or similar.
- Experience with Neo4j and Cypher is highly desirable.
- Strong PostgreSQL / SQL skills and experience with Redis.
- Experience with Node.js / TypeScript; Next.js / React is a plus.
- Experience with AWS and/or GCP, Docker and CI/CD.
- Strong understanding of APIs, asynchronous processing, testing and debugging.
- Good communication skills and ability to independently own technical problems.
GOOD TO HAVE
- Experience with LLM or vision model fine-tuning workflows, GPU-backed training and model registries.
- Computer vision with PyTorch / TensorFlow, OpenCV, YOLO or U-Net.
- Payment and real-time integrations such as Stripe, Razorpay, Socket.io or Pusher.
- Comfort with AI-assisted development tools such as Cursor or Claude Code.
QUALIFICATIONS
- Bachelor’s degree in Engineering (IT / Computer Science) or equivalent practical experience.
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