Job Description :

- 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 strong 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.