Lead AI/ML Engineer ( NLP, Transformers, Vector Databases, and RAG),

Optum India; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Optum India seeks a Lead AI/ML Engineer to drive NLP-focused AI solutions, leveraging transformer architectures, vector databases, embeddings, and RAG. You will work with senior data scientists, ML engineers, and software teams to push research into production.

You will lead LLM training and fine-tuning across open-source and closed ecosystems, architect GraphRAG pipelines, and advance semantic retrieval with strong grounding and indexing strategies.

Qualifications

• Masters in CS/AI or related field.
• Experience in Data Science, AI/ML and production-grade deployments.

Responsibilities

• Lead end-to-end training and fine-tuning of LLMs, including open- and closed-source ecosystems.
• Architect GraphRAG pipelines with knowledge graph grounding.
• Design and optimize semantic embeddings for document understanding, search and retrieval.
• Develop semantic retrieval systems with advanced segmentation and indexing.
• Apply algorithms in NLP, state-space models and agentic approaches.

Skills

NLP

Transformers

Vector Databases

RAG

LLM training

PyTorch

Python

SQL

Embeddings

Semantic Search

Weaviate/Milvus/FAISS

Education

Master's degree in Computer Science / AI / ML

Tools

PyTorch

Qwen

LLaMA

Mistral

OpenAI API

GPT

QLoRA

LoRA

Weaviate

Milvus

FAISS

Knowledge Graphs

Job description

The Lead AI/ML Engineer is a primary driver in the design and development of state-of-the-art Artificial Intelligence solutions with a strong focus on Natural Language Processing (NLP), Transformer-based architectures, Vector Database technologies, and Retrieval-Augmented Generation (RAG). The Lead AI/ML Engineer works closely with senior data scientists, machine learning engineers, software engineers, and subject matter experts on current company technologies and forward-looking projects. They drive research, innovation, and solution implementation in the creation of next-generation AI systems.

Key Responsibilities

• Lead end-to-end training and fine-tuning of Large Language Models (LLMs), including both open-source (e.g., Qwen, LLaMA, Mistral) and closed-source (e.g., OpenAI, Gemini, Anthropic) ecosystems.
• Deep knowledge and extensive experience with Machine/Deep Learning frameworks including transformer architectures, state space models, large language models, and agentic approaches.
• Architect and implement GraphRAG pipelines, including knowledge graph representation and retrieval for enhanced contextual grounding.
• Design, train, and optimize semantic and dense vector embeddings for document understanding, search, and retrieval.
• Develop semantic retrieval systems with advanced document segmentation and indexing strategies.
• Knowledge of algorithms and techniques within a computational domain with emphasis on text processing.

This keeps the JD exactly as is while making it immediately obvious that the role's primary focus is NLP, Transformers, Vector Databases, Embeddings, Retrieval, and RAG.

Preferred Qualifications

• Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• 11-15+ years of experience in Data Science, Applied AI/ML, and Statistical Modeling, with a strong track record of developing, deploying, and scaling production-grade AI solutions.

Deep expertise in:

• Natural Language Processing (NLP)
• Fundamental Machine Learning
• State Space-Based Architectures
• Strong Python programming, SQL, database querying, data preparation, and analysis skills
• Exploratory Data Analysis (EDA)
• Experience with PyTorch
• LLM training and fine-tuning (e.g., GPT, LLaMA, Mistral, Qwen)
• Semantic Search and Vector Databases (e.g., FAISS, Weaviate, Milvus)
• Model Fusion and Ensemble Techniques (Stacking, Boosting, Gating)
• Optimization Algorithms (Bayesian, Particle Swarm, Genetic Algorithms)
• Reinforcement Learning (e.g., RLHF, PPO, DPO, GRPO), Supervised Fine-Tuning (SFT), LoRA, QLoRA, Axolotl

Key focus areas:

NLP, Transformer-Based Architectures, Vector Database Technologies, Semantic Search, Embedding Models, and Retrieval-Augmented Generation (RAG).