Top AI & Machine Learning Jobs

Design, develop, validate, and deploy GenAI and traditional machine learning solutions for healthcare operations. Build scalable Python inference APIs on Kubernetes, implement semantic search, develop LLM and agentic applications, and maintain AI/ML pipelines with monitoring, documentation, and coding standards. Evaluate emerging technologies, promote responsible AI, and use AI-assisted development tools to improve software delivery. The role is fully telecommute within the United States.

Design, build, deploy, and maintain AI/ML solutions (including LLMs and Generative AI) for healthcare. Provide technical leadership for chatbots, conversational AI, and NLP systems; ensure responsible AI, security, and operational excellence; mentor team members and collaborate with product and engineering to integrate AI in cloud modernization efforts.

Lead end-to-end delivery of AI/ML programs: define roadmaps, manage cross-functional teams, ensure production reliability and compliance, govern responsible AI, control costs, and deliver measurable business outcomes.

Design, develop, and deploy production AI/ML solutions for underwriting and healthcare benefits modernization. Build RAG pipelines, integrate LLMs, support agentic AI using MCP and agent-to-agent protocols, and create secure, high-performance APIs for machine learning capabilities. Collaborate with software, data engineering, and underwriting teams to deliver scalable microservices and pipelines while ensuring model availability, security, monitoring, testing, and performance.

Leads data scientists, analysts, and engineers integrating pricing models into an enterprise pharmacy pricing platform. Provides technical leadership across solution design, platform integration, production delivery, analytics architecture, and software engineering. Manages resources, coaches team members, partners with product, engineering, and business leaders, drives continuous improvement, and communicates analytical results and predictive models. The role requires Python and SQL expertise, people management, enterprise production delivery, financial modeling, and Agile experience.

Architect and deploy AI-powered SaaS and full-stack applications using LLMs, generative AI, agentic workflows, microservices, APIs, and cloud technologies. Integrate React or Next.js frontends with Python/FastAPI or Node.js backends and enterprise databases. Lead AI-assisted development, establish responsible AI governance, ensure security and compliance, mentor developers, review code, and translate business needs into scalable solutions.

Lead architecture strategy and execution for portal, API, chat/voice, and data platforms. Drive AI/ML adoption and responsible AI, data strategy, governance, analytics, observability, security, and cross-team alignment while mentoring architects and ensuring production readiness.

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, develop, and deploy production AI and machine learning solutions for underwriting and healthcare benefits modernization. Build RAG pipelines, integrate LLMs and agentic AI capabilities, create secure high-performance APIs, and collaborate with software, data engineering, and underwriting teams. Optimize model performance, availability, security, monitoring, and testing while evaluating emerging AI technologies and supporting scalable ML platforms.

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.