Senior AI Engineer

Design and Develop AI Platform Services: Build reusable, scalable services that expose GenAI models, knowledge retrieval pipelines, and agent workflows to application teams.

Knowledge Base Development: Build and maintain knowledge retrieval systems. Process heterogeneous documents like pdfs and build embedding pipelines that extract, chunk and embed into embedding models.

AI Readiness: Process content from unstructured documents and make it AI ready: Optimize their structure, clear up text ambiguity, and embed rich machine-readable metadata.

AI Ops, evals and observability: Set up framework for monitoring and evaluating AI output quality (relevance, accuracy, safety, drift, cost) and platform observability (latency, cost, usage).

Centre of Excellence: Act as a centre of excellence for the whole business in the technical side of AI and LLMs usage, setting best practices and accelerating adoption.

Governance and Guardrails: Create governance layers for implementing PII redaction, prompt filtering etc.

Deep GenAI & RAG Expertise: 2+ years of hands-on experience building and productionizing LLM applications, with deep technical knowledge of vector databases (e.g., Qdrant, Weavite, Pinecone, pgvector) and orchestration layers (e.g., LangGraph, LlamaIndex). Comfortable working with heterogeneous documents like pdfs. Experience working with re-ranking models.

Production-Grade Asynchronous Python: Strong software engineering fundamentals with deep experience in asynchronous programming (asyncio, FastAPI) to handle highly concurrent, I/O-bound LLM and database calls. 

Advanced Context Engineering: Proven experience solving production RAG challenges such as context window management, metadata filtering and semantic routing. Experience leveraging coding harnesses like Claude code.

Modern Software Practices: Mastery of Git, automated testing and CI/CD pipelines. Experience working with cloud platforms like AWS.

Comfort with Ambiguity: Ability to rapidly prototype an approach, validate it with metrics, and pivot fast in a fast-moving environment.

GraphRAG Experience: Familiarity with Knowledge Graphs (e.g., Neo4j) and ontologies to enhance standard vector RAG with highly structured, interconnected corporate data.

Local/Open-Weight Model Deployment: Experience deploying and fine-tuning open-weight models (like Llama 3 or Mistral) via frameworks like vLLM or Ollama to optimize token costs and privacy.

Prompt Caching & Cost Optimisation: Practical experience implementing semantic caching layers to drastically reduce LLM API billing and response latency.