Role Overview

We are looking for a Senior AI Engineer with deep specialisation in Retrieval-Augmented Generation (RAG) and embedding systems. The ideal candidate has a proven track record of building end-to-end AI solutions - from ideation through to production deployment - and thrives at the intersection of language models, vector search, and applied NLP.

This is a hands-on, high-ownership role. You will design and ship RAG pipelines, own embedding infrastructure, integrate AI capabilities into chatbot products, and work with diverse data sources to build systems that actually work in production.

Responsibilities
RAG pipeline ownership - Ideate, architect, build, and deploy end-to-end RAG systems from scratch through to production
Embedding systems - Select, evaluate, and fine-tune embedding models; manage vector stores and optimise retrieval quality
Advanced chunking - Implement late chunking and other segmentation strategies to maximise context fidelity and retrieval precision




Multi-source data integration - Connect and ingest from diverse sources including SQL/NoSQL databases, PDFs, web content, Confluence, SharePoint, and real-time APIs
Chatbot integration - Embed RAG and LLM components into conversational AI products using LangChain, LlamaIndex, or custom orchestration layers
Evaluation & quality - Own retrieval evaluation frameworks (RAGAS, triad evals) and iterate on pipelines based on precision, recall, and relevance metrics
Deployment & observability - Deploy and monitor LLM services on cloud infrastructure with robust logging, alerting, and MLOps practices
Collaboration - Partner with product and engineering teams to deliver low-latency, reliable AI experiences at scale

Requirements
3+ years of total engineering experience, with 2+ years in LLM or NLP engineering
Hands-on experience designing and deploying RAG systems end-to-end
Deep familiarity with embedding models (OpenAI Ada, Cohere, BGE, E5) and vector databases (Pinecone, W

📌 Senior Ai Engineer Gurugram
🏢 Verbaflo
📍 Gurugram