RAG/LLM Engineer

EXL; Bengaluru; Hybrid; INR 2,500,000 - 4,500,000; Full time

Job summary

EXL is seeking a senior ML/GenAI Engineer in Bengaluru to lead the design and optimization of advanced RAG pipelines and model fine-tuning processes. You will bridge the gap between prototype and enterprise-scale LLM deployment, ensuring high relevance and low latency.

You will architect vector strategies, guide junior analysts in prompt engineering, and drive evaluation frameworks to continually improve context accuracy and recall.

Qualifications

• 4–7 years in ML/AI, including 1+ years with LLMs.
• Bachelor's or Master's in CS/Data Science.
• Experience with RAG architectures, PEFT/LoRA, and prompt optimization.

Responsibilities

• Design and own multi-stage RAG pipelines with low latency and high relevance.
• Lead fine-tuning initiatives (PEFT/LoRA) for open-source models.
• Develop automated evaluation frameworks to measure LLM accuracy and recall.
• Architect vector strategies in Pinecone or Milvus for efficient retrieval.
• Mentor junior analysts in prompt engineering and code quality.

Skills

Python

PyTorch

TensorFlow

LangChain

LlamaIndex

Embedding models

RAG architectures

Prompt optimization

PEFT/LoRA

Education

Bachelor's in CS/Data Science

Master's in CS/Data Science

Tools

Pinecone

Milvus

LangChain

LlamaIndex

Job description

Role Overview: Lead the design and optimization of advanced RAG pipelines and model fine tuning processes. Bridge the gap between prototype and enterprise-scale LLM deployment.

Key Responsibilities

• Pipeline Ownership: Design and manage complex, multi-stage RAG pipelines ensuring low latency and high relevance.
• Model Optimization: Lead fine-tuning initiatives (PEFT/LoRA) for open-source models to improve domain-specific task performance.
• Advanced Evaluation: Develop automated evaluation frameworks (e.g., RAGAS) to continually measure LLM accuracy, context precision, and recall.
• Vector Strategy: Architect metadata filtering and hybrid search strategies within vector databases (e.g., Pinecone, Milvus).
• Team Mentorship: Guide junior analysts in prompt engineering, chunking strategies, and code quality.

Required Skills & Qualifications

• Tech Stack: Python, PyTorch/TensorFlow, LangChain, LlamaIndex, advanced embedding models.
• GenAI Skills: Deep expertise in advanced RAG (HyDE, parent-document retrieval), prompt optimization, and parameter-efficient fine-tuning.
• Qualifications: Bachelors/Master’s in CS/Data Science with 4–7 years in ML/AI, including 1+ years specifically working with LLMs.