AI/ML Senior Engineer

Role Summary Looking for an experienced AI/ML Architect to lead our AI/ML architecture, agentic AI platform design, and applied AI research initiatives for healthcare payer operations. This is a senior technical leadership role for someone who can combine hands-on AI/ML expertise, healthcare payer understanding, architecture depth, and research leadership to help build next-generation AI solutions for utilization management, care management, claims, provider operations, pharmacy, and member engagement. Role Overview The AI/ML Architect will lead the design and evolution of organization's AI/ML capabilities, including machine learning models, payer-specific LLMs, retrieval-augmented generation, intelligent agents, and agentic AI workflows. This role will also lead company’s AI/ML Research Frontier Team, focused on exploring emerging AI methods and translating them into scalable, production-ready payer solutions. Key Responsibilities AI/ML Architecture Leadership • Define company’s AI/ML architecture roadmap across products and platforms. • Design scalable architectures for ML models, LLM applications, RAG pipelines, and agentic workflows. • Establish reusable patterns for model development, inference, evaluation, monitoring, and governance. • Ensure AI/ML solutions are secure, modular, explainable, and deployable within payer environments. Agentic AI & Workflow Design • Architect modular AI agents for payer workflows such as prior authorization, medical necessity review, policy summarization, risk prediction, care management, provider service, and member service. • Design multi-agent workflows across UM, CM, claims, pharmacy, provider, and member operations. • Guide the evolution of company’s Agentic Operating Platform for cross-domain orchestration. AI/ML Research Frontier Leadership • Lead research workstreams in LLMs, agentic AI, clinical NLP, graph intelligence, predictive modeling, digital twins, multimodal AI, and synthetic data. • Evaluate emerging AI techniques and convert promising research into product-ready capabilities. • Partner with academic, research, and engineering teams to accelerate innovation. Technical Leadership & Governance • Mentor AI/ML engineers, data scientists, data engineers, and application teams. • Review architecture, model design, implementation plans, and technical standards. • Establish MLOps and LLMOps practices for CI/CD, model registry, evaluation, observability, and monitoring. • Support secure cloud or payer-environment deployment across Azure, AWS, or client-managed infrastructure. Required Experience • Hands-on Experience in software engineering, data science, AI/ML, or enterprise architecture. • Hands-on Experience in designing and deploying AI/ML solutions in production. • Strong hands-on experience with ML, deep learning, NLP, LLMs, RAG, and AI application architecture. • Experience with Python and frameworks such as PyTorch, TensorFlow, Scikit-learn, Hugging Face, LangChain, LlamaIndex, or Semantic Kernel. • Experience with vector databases and search technologies such as pgvector, Qdrant, Weaviate, Milvus, OpenSearch, or Elasticsearch. • Strong understanding of MLOps, LLMOps, model monitoring, AI governance, and responsible AI practices. • Ability to communicate complex AI concepts to executives, product teams, engineers, and healthcare stakeholders. Preferred Experience • Experience leading research teams or translating AI research into commercial products.