Principal Data Scientist

Optum India; Bengaluru; On-site; INR 6,000,000 - 12,000,000; Full time

Job summary

Optum India is seeking a senior AI leader to design, lead, and deploy Large Language Model solutions across open-source and closed-source ecosystems. The role requires deep expertise in NLP, transformers, and knowledge-graph-based retrieval to drive end-to-end AI initiatives in production environments.

The candidate should have extensive experience (11-15+ years) in applied AI/ML, a strong publication record in text extraction and summarization, and hands-on proficiency with PyTorch, vector

Qualifications

• Master's degree in computer science, ML, or related field.
• 11-15+ years of experience in applied AI/ML with statistics.
• Deep expertise in NLP, fundamental ML, deep learning, transformer, state-space architectures.
• Strong Python coding, SQL and data preparation/analysis.
• Exploratory Data Analysis (EDA).
• Experience with PyTorch.
• Graph-based retrieval systems (knowledge graphs).
• Semantic search and vector databases (FAISS, Weaviate, Milvus).
• Model fusion and ensemble techniques (stacking, boosting, gating).
• Optimization algorithms (Bayesian, Particle Swarm, Genetic Algorithms).
• Reinforcement learning (RLHF, PPO, DPO, GRPO), SFT, LoRA, QLoRA, axolotl.

Skills

Python

NLP

Transformers

Graph retrieval

RLHF

EDA

PyTorch

Knowledge graphs

Education

Master's degree in computer science / ML

Tools

PyTorch

FAISS

Weaviate

Milvus

NCCL

InfiniBand

Job description

• Deep knowledge and extensive experience with Machine/Deep Learning frameworks including transformer architectures, state space models, large language models, and agentic approaches
• Lead end-to-end development and implementation of Large Language Model (LLM) solutions across both open-source (e.g., Qwen, LLaMA, Mistral) and closed-source (e.g., OpenAI, Gemini, Anthropic) ecosystems.
• Knowledge of algorithms and techniques within a computational domain with emphasis on text processing
• Demonstrated publication record in AI domain especially relating to text extraction and summarization
• 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.
• Build and scale distributed training environments using NCCL and InfiniBand for multi-GPU and multi-node training.
• Apply reinforcement learning techniques (e.g., RLHF, RLAIF) to align model behavior with human preferences and domain-specific goals.
• Experience with Hybrid NLP solutions that combine symbolic and machine learning approaches
• Collaborate with cross-functional teams to translate business needs into AI-driven solutions and deploy them in production environments.

Required Qualifications

• Master's degree in computer science, Machine Learning, or related field.
• 11-15+ years of experience in applied AI/ML with statistics, with a strong track record of delivering production-grade models.
• Deep expertise in: NLP, Fundamental machine learning, deep learning, transformer, state space-based architecture
• Strong in Python coding, SQL and database queries, data preparation, and analysis
• Exploratory Data Analysis (EDA)
• Experience with PyTorch
• Graph-based retrieval systems (knowledge graphs)
• 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