Primary 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
- Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so
Required Qualifications:
- 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
- Demonstrated publication record in AI domain especially relating to text extraction and summarization
- Experience with Hybrid NLP solutions that combine symbolic and machine learning approaches
Preferred Qualifications
- PhD or master’s degree in computer science, Machine Learning, or related field
- 12+ 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
- Exploratory Data Analysis (EDA)
- Experience with PyTorch
- Experience with LLM training and fine-tuning (e.g., GPT, LLaMA, Mistral, Qwen)
- Experience with graph-based retrieval systems (GraphRAG, knowledge graphs)
- Experience with embedding models (e.g., BGE, E5, SimCSE)
- Experience with semantic search and vector databases (e.g., FAISS, Weaviate, Milvus)
- Experience with document segmentation and preprocessing (OCR, layout parsing)
- Experience with distributed training frameworks (NCCL, Horovod, DeepSpeed)
- Experience with high-performance networking (InfiniBand, RDMA)
- Experience with model fusion and ensemble techniques (stacking, boosting, gating)
- Experience with optimization algorithms (Bayesian, Particle Swarm, Genetic Algorithms)
- Experience with Symbolic AI and rule-based systems
- Experience with meta-learning and Mixture of Experts architectures
- Experience with reinforcement learning (e.g., RLHF, PPO, DPO, GRPO), Supervised Fine Tuning (SFT), LoRA, QLoRA, axolotl
- Experience with prompt optimization framework (AutoPrompt, GreaterPrompt, DSPy), GEPA
- Proven solid in Python coding, SQL and database queries, data preparation, and analysis
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
