Data Scientist

Job Title

Must Have: Multi model agents Experience with text-to-image, image-to-text, speech-to-text Published papers in Machine Learning/Data Science journals Medical background project

Architect and implement GraphRAG pipelines, including knowledge graph representation and retrieval for enhanced contextual grounding.

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.

Qualifications

• 8+ years of experience in applied AIML, 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.