:

- Develop 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.
- Knowledge of algorithms and techniques within a computational domain with emphasis on text processing.
- 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.

Qualifications :

- Graduate degree or equivalent experience.
- PhD or Masters degree in computer science, Machine Learning, or related field.
- 10+ years of experience in applied AI/ML with statistics, with a robust track record of delivering production-grade models.
- Deep expertise in NLP, Fundamental machine learning, deep learning, transformer, state space-based architecture.
- Azure ML and/or AWS.
- Strong in Python coding, SQL and database queries, data preparation, and analysis.
- Exploratory Data Analysis (EDA).
- Experience with PyTorch.
- LLM training and fine-tuning (e.g., GPT, LLaMA, Mistral, Qwen).
- Graph-based retrieval systems (GraphRAG, knowledge graphs).
- Embedding models (e.g., BGE, E5, SimCSE).
- Semantic search and vector databases (e.g., FAISS, Weaviate, Milvus).
- Document segmentation and preprocessing (OCR, layout parsing).
- 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.

Education :

- UG : Any Graduate.

📌 Optum - Senior AI/ML Engineer (India)
🏢 Optum Global Solutions
📍 India