Top AI & Machine Learning Jobs

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.

Lead enterprise AI strategy, architecture, and delivery to scale generative AI, LLMs, agents, and reusable AI platforms. Build and run multidisciplinary teams, establish governance and MLOps/LLMOps practices, partner with stakeholders to translate business priorities into production AI capabilities, and ensure responsible, secure adoption with measurable business outcomes.

Build and deploy AI-powered, LLM-based solutions in client environments. Implement integrations between models, APIs, and healthcare systems, write production-ready code, support testing and deployments, assist data preparation and prompt configuration, troubleshoot issues, and collaborate with cross-functional teams and clients to deliver scalable, secure solutions and enable knowledge transfer.

Lead design of AI-powered chat and voice experiences by defining conversation strategy, architecture, and persona design. Provide thought leadership, mentor designers, establish prompt-engineering and conversation-design best practices, integrate AI tools into product workflows, enable personalization, align stakeholders, and measure/optimize conversational performance while ensuring accessibility and health-literacy standards.

Lead technical direction and people management for an AI/ML engineering squad. Design, build, and deploy production-grade ML and Generative AI solutions (LLMs, RAG, agentic systems), drive MLOps/CI-CD, ensure Responsible AI and HIPAA-compliant practices, conduct architecture/code reviews, and mentor engineers to deliver measurable business outcomes.

Design, build, operate, and maintain enterprise AI platform services and agent runtimes. Develop APIs, SDKs, MCP servers, tool interfaces, retrieval and embedding pipelines, model serving, evaluation infrastructure, and distributed data foundations. Establish engineering, security, testing, CI/CD, and infrastructure standards while optimizing platform cost and scale. Apply AI/ML capabilities to healthcare workflows, ensure PHI protection and tenant isolation, evaluate emerging technologies, and mentor engineers while influencing technical strategy.

Lead the architecture and end-to-end development of production-grade AI/ML systems, including training, deployment, monitoring, MLOps, deep learning, NLP, ASR, and agentic AI workflows. Translate research into scalable enterprise solutions, establish engineering and governance standards, evaluate emerging tools, communicate technical tradeoffs to leadership, and mentor engineers. The role requires expertise in Python, deep learning frameworks, cloud ML platforms, ML pipelines, statistics, and production LLM or agentic systems.

Lead data science projects from design through implementation, delivering statistical and advanced analytics solutions with minimal supervision. Collaborate with data science leads, business partners, and healthcare stakeholders to understand data, design solutions, operationalize analytics, and communicate progress. Apply regression, time-series analysis, clustering, decision trees, SVM, gradient boosting, and NLP using Python, Azure, and Databricks to solve complex healthcare business problems.