Top AI & Machine Learning Jobs

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.

Leads enterprise data engineering and AI platform strategy, architecture, governance, and execution. Oversees data engineering teams, cloud modernization, pipelines, data products, APIs, streaming, feature stores, vector databases, RAG, and AI lifecycle operations. Establishes data quality, lineage, security, responsible AI, and compliance standards while managing budgets, vendors, KPIs, and organizational talent development. Partners with executives and business leaders to deliver scalable AI capabilities and measurable business value.

Leads end-to-end integration, architecture, development, validation, and deployment of AI-powered enterprise applications in the Clinical AI domain. Translates business needs into scalable solutions, evaluates technology options, manages technical workstreams, and ensures security, privacy, compliance, observability, resilience, and responsible AI standards. Coordinates proofs of concept and production readiness while mentoring engineers and advancing automation, AI-assisted workflows, and engineering best practices.

Lead product capabilities within an AI product portfolio: define roadmaps, prioritize features, partner with engineering and design, drive Agile delivery, use customer insights and metrics to improve adoption, mentor junior product managers, and ensure alignment of technical feasibility with business outcomes.

Design, build, deploy, and operate AI-powered full-stack applications following an AI Development Lifecycle. Implement frontend (React/Next.js/TypeScript) and backend (Node.js/Python) services, integrate LLMs, RAG, vector search, and MLOps/CI-CD pipelines, ensure security, observability, and production readiness, and mentor engineers.

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.

Leads the architecture, development, deployment, and governance of production-grade machine learning and generative AI solutions. Responsibilities include hands-on work with traditional ML, deep learning, LLMs, NLP or computer vision, MLOps, cloud infrastructure, model security, and responsible AI. The role leads proof-of-concept initiatives, transitions models into scalable production systems, mentors technical teams, documents architecture, and advises cross-functional stakeholders and leadership.