Role

- 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
- 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
- Collaborate with cross-functional teams to translate business needs into AI-driven solutions and deploy them in production environments

AsK:

- Graduate degree or equivalent experience
- PhD or Master’s degree in computer science, Machine Learning, or related field
- 10+ years of experience in applied AI/ML with statistics, with a Solid track record of delivering production-grade models
- Experience with Hybrid NLP solutions that combine symbolic and machine learning approaches
- Experience with PyTorch




- 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
- Proven deep expertise in: NLP, Fundamental machine learning, deep learning, transformer, state space-based architecture
- Azure ML and/or AWS
- Proven solid in Python coding, SQL and database queries, data preparation, and analysis
- Exploratory Data Analysis (EDA)
- 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
- Prompt optimization framework (AutoPrompt, GreaterPrompt, DSPy), GEPA

📌 Senior AIML Engineering Leader - GenAI & Agentic AI @ Hyderabad/Bengaluru70L/Need from Product orgs (Bengaluru)
🏢 LiveConnections
📍 Bengaluru