Senior AI/ML Engineer

•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