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

Optum India; Hyderabad; Vor Ort; INR 4.500.000 - 9.000.000; Vollzeit

Zusammenfassung

Optum India is seeking a Lead AI/ML Engineer to drive state-of-the-art NLP and transformer-based AI solutions. You will lead end-to-end LLM training, integrate vector databases, and build RAG-powered retrieval systems.

The role collaborates with data scientists, ML engineers, and software teams on ambitious projects. The candidate brings deep NLP expertise, strong Python/SQL skills, and a proven record of deploying production AI at scale, with preference for HDRP/edge deployments and enterprise

Qualifikationen

• Masters degree in CS/ML/AI or related field.
• 11-15+ years of experience in Data Science, Applied AI/ML, and statistical modeling.
• Strong track record of developing, deploying, and scaling production-grade AI solutions.

Aufgaben

• Lead end-to-end training and fine-tuning of LLMs across open- and closed-source ecosystems.
• Architect and implement GraphRAG pipelines with knowledge graphs and retrieval grounding.
• Design, train, and optimize semantic and dense vector embeddings for document understanding.
• Develop semantic retrieval systems with advanced segmentation and indexing strategies.
• Drive research, innovation, and scalable AI solution delivery.

Kenntnisse

NLP

Transformer architectures

Python programming

SQL

EDA

PyTorch

LLM training/fine-tuning

Vector databases

Semantic search

Retrieval-Augmented Generation

Ausbildung

Master's degree in CS/ML/AI

Tools

FAISS

Weaviate

Milvus

PyTorch

Jobbeschreibung

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).