AI Engineer (LLM / NLP / RAG)

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.

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. .