Top AI & Machine Learning Jobs

Leads enterprise software engineering strategy, AI and machine learning adoption, cloud platform architecture, legacy modernization, and generative AI deployments. Oversees engineering managers and principal engineers, establishes governance and operational standards, mentors technical teams, and aligns technical roadmaps with executive business goals. The role also drives ethical AI practices, MLOps governance, scalable software delivery, organizational hiring strategies, and cross-functional technology transformation across healthcare systems.

Design, deploy, and operate production AI agents and generative AI solutions using Azure OpenAI, Snowflake, Databricks, retrieval, semantic models, and tool orchestration. Build reliable data workflows, governance, security, monitoring, evaluation, and responsible AI controls for sensitive healthcare data. Integrate AI systems with enterprise applications and APIs, troubleshoot performance and retrieval issues, and collaborate with technical and business stakeholders to deliver compliant, scalable solutions.

Leads the development and production deployment of machine learning, generative AI, LLM, RAG, and agentic AI solutions for pharmacy and healthcare operations. Builds predictive models, scalable data pipelines, AI services, vector search systems, and healthcare analytics using large clinical, claims, EHR, provider, member, and operational datasets. Establishes model monitoring, evaluation, guardrails, responsible AI practices, and regulatory compliance while collaborating with clinical, product, business, data, and engineering teams.

Leads the architecture, development, deployment, and operation of enterprise AI Ops solutions, including RAG pipelines, agentic workflows, and AI-powered applications. Improves SRE practices, leads incident response, automates cloud infrastructure with Terraform and GitHub Actions, and ensures reliability, scalability, security, and responsible AI. Designs and operates software using Python and Node.js, manages Kubernetes environments, mentors engineers, and leads globally distributed technical teams.

Provides technical leadership for enterprise Generative AI and Agentic AI initiatives. Designs scalable AI platforms, foundation-model solutions, RAG and knowledge systems, multi-agent workflows, and LLMOps capabilities. Establishes architecture standards, governance, evaluation, observability, security, and responsible AI practices while optimizing quality, performance, scalability, and cost. Leads complex delivery, mentors AI professionals, influences enterprise roadmaps, and drives adoption of reusable frameworks and shared services.

Develop and evaluate novel AI/ML solutions for healthcare using structured and unstructured data. Build and test deep learning and classical ML models, design experiments, perform statistical analysis and visualizations, document models, collaborate with stakeholders, RUAI, and engineering to productionize solutions and MLOps pipelines, and publish/patent research.

Lead enterprise AI/ML engineering for Medicare & Retirement, defining AI strategy, building scalable MLOps platforms, embedding GenAI and ML into products and operations, ensuring responsible AI and compliance, and delivering measurable business value through production-grade AI solutions and platform transformation.

Lead design, development, and deployment of GenAI and NLP solutions using LLMs, vector search, and RAG. Architect agentic AI workflows, optimize inference and pipelines, perform advanced data preprocessing and feature engineering, and collaborate across research, engineering, and product teams to productionize ethical, scalable AI systems.

Design and execute test strategies for AWS-based voice bots/IVR and LLM-driven intent engines. Develop automated test frameworks, validate conversational flows and intent recognition, run functional/regression/integration/performance tests, monitor CloudWatch logs/metrics, track defects, and improve QA within CI/CD pipelines.

Lead product strategy and roadmap for an enterprise AI modernization of medical record review. Align business, clinical, and technical stakeholders to integrate LLMs, intelligent document understanding, and autonomous coding across Optum/UnitedHealthcare systems, define success metrics, ensure compliance and responsible AI, and drive adoption and operational outcomes.