AI Engineer (LLM / NLP / RAG)

You will join the AI engineering team of a large global financial services enterprise. Your core focus will be designing and building retrieval-augmented systems that allow users to accurately query massive internal document estates. This is a hands-on, product-facing role with early exposure to emerging agent-based architectures.

Responsibilities:

• LLM and GenAI: Build applications using GPT, Llama, Gemini, or Claude; optimise prompts, embeddings, and inference workflows.
• RAG Pipelines: Develop and maintain high-performing retrieval pipelines (chunking, vector search, response relevance).
• Agentic Systems: Implement agent coordination, tool integrations, and orchestration logic using LangChain or Google ADK.
• Core NLP: Design and implement NLP pipelines, data preprocessing, and model evaluation metrics.
• Deployment: Build reusable, scalable AI services and REST APIs using Docker and Cloud platforms.

Requirements:

• Experience: 5+ years in software engineering, with 3 years in ML/NLP and 1.5+ years building LLM applications.
• Python: Production-grade, strong coding skills.
• Vector DBs: Hands-on with at least one (Elasticsearch, OpenSearch, FAISS, etc. ).
• NLP Foundations: Solid grounding in tokenisation, embeddings, and transformers.
• DevOps: Experience with Docker, microservices, and any major cloud (AWS, Azure, GCP, OCP).

Nice-to-Have:

• Experience with agent workflows and tool-based reasoning.
• Familiarity with PyTorch, TensorFlow, MLOps tooling, or Kubernetes.
• Exposure to the Financial Services / BFSI domain.