AI/ML Engineer

Work Experience

• 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.

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.
• Ability to take research papers to PoC production.