AI/ML Engineer

Key Responsibilities • Lead 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. Preferred Qualifications • PhD or Master’s degree in Computer Science, Machine Learning, or related field. • 8+ years of experience in applied AI/ML, with a strong track record of delivering production-grade models. • Deep expertise in: • 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) • Distributed training frameworks (NCCL, Horovod, DeepSpeed) • High-performance networking (InfiniBand, RDMA) • Model fusion and ensemble techniques (stacking, boosting, gating) • Optimization algorithms (Bayesian, Particle Swarm, Genetic Algorithms) • Symbolic AI and rule-based systems • Meta-learning and Mixture of Experts architectures • Reinforcement learning (e.g., RLHF, PPO, DPO) Bonus Skills • Experience with healthcare data and medical coding systems (e.g., CPT, CM, PCS). • Familiarity with regulatory and compliance frameworks in AI deployment. • Contributions to open-source AI projects or published research. And/Or ability to take research papers to poc – production.