Join a global organization dedicated to improving health outcomes through innovative technology. This role focuses on advancing health optimization on a global scale by connecting people with essential care, pharmacy benefits, data, and resources. You'll be part of an inclusive culture with talented colleagues, comprehensive benefits, and abundant career development opportunities, all while making a significant impact.

Optum is committed to helping millions live healthier lives by delivering care aided by cutting-edge technology. The work performed directly enhances health outcomes, ensuring individuals have access to the care, pharmacy benefits, data, and resources they need to thrive. We foster an environment of inclusion, collaboration, and continuous growth, offering a platform for you to make a meaningful difference in the communities we serve.

Spearhead the design, development, and deployment of sophisticated GenAI and NLP solutions, leveraging state-of-the-art transformer models and LLMs to tackle complex business and healthcare challenges. Lead the research, evaluation, and integration of novel LLMs (GPT, Claude, Gemini, Anthropic) and vector search technologies, alongside advanced prompt-engineering techniques and enterprise GenAI platforms.

Build and refine Retrieval-Augmented Generation (RAG) systems using vector databases (FAISS, Pinecone, Chroma) to enhance grounding and retrieval accuracy. Architect and implement agentic AI workflows using frameworks like LangGraph, AutoGen, CrewAI, or Semantic Kernel, facilitating sophisticated reasoning and planning in single- and multi-agent systems.

Develop high-quality NLP pipelines, including tokenization, embeddings, seq-to-seq models, and evaluation metrics, with a strong emphasis on performance and scalability. Conduct advanced data analysis, preprocessing, and feature engineering to ensure data integrity and model reliability. Create and manage scalable big-data pipelines for data ingestion and processing, and optimize model inference for low latency and high throughput.

A Bachelor's degree in Computer Science or Engineering with a specialization in language processing is required, though candidates with a PhD may have this requirement relaxed. A minimum of 7 years of experience in AI, ML, and/or NLP R&D with demonstrable impact is preferred, alongside 10+ years of overall professional experience.

Proficiency is expected in both traditional machine learning algorithms and modern deep learning and generative AI models. Strong hands-on skills with repository management, GPU utilization, and the ability to rapidly set up pipelines for testing new ideas are crucial. Expertise in Python, R, SQL, and other data analysis programming languages is essential.

Demonstrated experience with cloud development environments such as Azure, AWS, and GCP is required. Excellent analytical and problem-solving skills are necessary, including the capacity to dissect complex issues, identify root causes, and propose effective solutions.