Primary responsibilities include the enhancement of existing company NLP technologies and extension of those systems in new cloud-based applications. Emphasis is on development of novel machine/deep learning techniques for information extraction and synthesis. They translate research code into clinical NLP solutions deployed at scale in production environments including statistical methods, deep learning, and large language model technologies. Work will involve all aspects of methods development from initial PoC implementation to performance characterization and production launch of recent methods.
The successful candidate will have a strong history of publication in Machine/Deep Learning with an emphasis on Natural Language Processing, Information Retrieval and/or Information Extraction. Exposure to recent research literature and the ability to effectively implement new technologies is key. The successful candidate will have proven success in taking machine/deep learning solutions to production environments. Strong technical skills are required.
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.
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
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 strate

📌 Lead AI/ML Engineer (Hyderabad)
🏢 Optum
📍 Hyderabad