RAG/LLM Specialist Manager/Sr. Manager (Bengaluru)

EXL; Bengaluru; On-site; INR 2,500,000 - 3,500,000; Full time

Job summary

EXL in Bengaluru, India is seeking a Senior Manager RAG/LLM Specialist to lead design, optimization, and enterprise deployment of Retrieval-Augmented Generation (RAG) solutions and LLM-based applications.

The ideal candidate has hands-on experience with RAG pipelines, LLM fine-tuning, embeddings, vector databases, prompt engineering, and GenAI evaluation frameworks, with the ability to take solutions from prototype to production at enterprise scale.

Qualifications

• 47 years of experience in AI/ML, NLP or software engineering with at least 1 year in LLM/GenAI.
• Hands-on experience designing and deploying RAG solutions at scale.
• Strong Python skills and experience with vector databases and PEFT techniques.

Responsibilities

• Design and manage multi-stage RAG pipelines for high relevance and low latency.
• Lead LLM fine-tuning initiatives using PEFT, LoRA/QLoRA and other techniques.
• Collaborate with data, engineering and product teams to productionize LLM solutions.
• Mentor junior team members on RAG architecture and evaluation methods.
• Develop automated evaluation frameworks to measure context precision and recall.

Skills

Python programming

RAG pipelines

LLM fine-tuning

Prompt engineering

GenAI evaluation

Tools

Pinecone

Milvus

FAISS

Weaviate

Qdrant

Job description

Senior Manager RAG/LLM Specialist

Experience: 47 Years

Function: Generative AI / Machine Learning / LLM

Employment Type: Full-time

Role Overview

We are looking for a Senior Manager RAG/LLM Specialist to lead the design, optimization, and enterprise deployment of advanced Retrieval-Augmented Generation (RAG) solutions and LLM-based applications.

The ideal candidate will have solid hands-on experience in RAG pipelines, LLM fine-tuning, embeddings, vector databases, prompt engineering, and GenAI evaluation frameworks, with the ability to take solutions from prototype to production at enterprise scale.

Key Responsibilities

• Design and manage complex, multi-stage RAG pipelines focused on high relevance, accuracy, scalability, and low latency.
• Lead LLM fine-tuning initiatives using PEFT, LoRA/QLoRA, and other parameter-efficient techniques.
• Work with open-source foundation models to improve domain-specific performance.
• Design advanced retrieval strategies including hybrid search, semantic search, metadata filtering, reranking, HyDE, and parent-document retrieval.
• Develop automated evaluation frameworks using tools such as RAGAS to measure context precision, context recall, response quality, and overall LLM performance.
• Architect and optimize vector database solutions using technologies such as Pinecone, Milvus, FAISS, Weaviate, or Qdrant.
• Lead prompt engineering and optimization initiatives across GenAI use cases.
• Work closely with data, engineering, and product teams to productionize LLM solutions.
• Mentor junior team members on RAG architecture, chunking strategies, prompt engineering, evaluation, and code quality.
• Drive best practices around LLM application development, observability, testing, and performance optimization.

Required Skills

• 47 years of experience in AI/ML, Data Science, NLP, or Software Engineering, with at least 1+ year of hands-on LLM/GenAI experience.
• Strong programming skills in Python.
• Hands-on experience with RAG / Re .