Top AI & Machine Learning Jobs

Design, develop, and maintain production-scale NLP and AI solutions for clinical applications. Enhance clinical lexicons, ontologies, and rule systems; evaluate component performance on clinical documentation; generate domain analytics; collaborate with engineers to implement, integrate, test, and support NLP systems in production environments.

Design, build, and deploy AI/ML and generative AI solutions and full-stack applications. Develop frontend experiences, backend services, microservices, APIs, and cloud-native data integrations. Implement RAG, LLMs, vector DBs, dashboards, reporting, and self-service analytics to support quality, patient safety, and workflow automation. Provide technical leadership, mentor engineers, and apply CI/CD, testing, observability, security, and performance best practices.

Lead design, prototyping, and deployment of enterprise AI solutions (Generative/Agentic AI, LLMs, digital workers). Partner with business and engineering to scale AI, establish governance/responsible AI practices, and drive automation and measurable outcomes across platforms.

Design, build, and deploy scalable AI/ML and generative AI solutions (including RAG and agentic workflows) for sales and distribution modernization. Integrate APIs and microservices with enterprise systems (CRM, enrollment, broker ecosystems), implement MLOps, monitor model performance, ensure Responsible AI and security compliance, collaborate with product, engineering, offshore teams, and drive high-quality execution.

Lead enterprise AI strategy, architecture, and delivery to build scalable AI platforms and products. Oversee multidisciplinary teams in AI engineering, product, and architecture; govern responsible AI adoption; drive generative AI, LLM, and agent-based solutions into production while ensuring platform reuse, MLOps/LLMOps practices, and measurable business outcomes.

Lead multiple AI engineering teams to build and scale enterprise-grade AI, Generative AI, and Agentic AI solutions for claims automation and intelligent workflows. Drive platform strategy, cloud-based deployments, responsible AI governance, model lifecycle management, and cross-functional delivery. Mentor engineers, ensure operational excellence, integrate AI into cloud modernization, and align initiatives with enterprise AI strategy and business outcomes.

Design, build, and deploy scalable AI/ML solutions and end-to-end ML pipelines (ingestion to deployment). Implement MLOps/LLMOps, RAG/LLM applications, agentic workflows, model monitoring, and integrate solutions into production using cloud CI/CD.

Lead technical direction and people for an AI/ML engineering squad to design, build, and deploy production-grade ML and Generative AI (LLMs, RAG, agentic systems). Drive MLOps, CI/CD, model monitoring, Responsible AI compliance (HIPAA/PHI), architecture reviews, and mentor 5–10 engineers to deliver secure, production solutions.

Lead design and delivery of GenAI, LLM and agentic AI capabilities for the Vetting Agent. Define LLM strategy, prompt/agent design, RAG grounding, evaluation frameworks, governance, and human-in-the-loop controls. Partner with engineering and product to productionize scalable, auditable AI agents and mentor data science teams.

Lead engineer to design, build, and operationalize AI/ML solutions and agentic automation for IT operations. Responsibilities include data pipeline and feature engineering, model deployment and monitoring, integrating AI outputs into orchestration platforms, cloud-native infrastructure, and applying Responsible AI and MLOps practices to improve observability, incident response, and self-healing workflows.