Top AI & Machine Learning Jobs

Lead and evolve the enterprise AI risk and governance framework. Identify and assess emerging AI risks, translate them into governance and controls, run AI approval processes, provide second-line challenge to stakeholders, partner across functions to embed controls, and strengthen monitoring, transparency, and scalable governance for AI initiatives.

Develops production-grade data science and analytics solutions across broad business problems. Responsibilities include statistical analysis, hypothesis testing, feature engineering, machine learning modeling, NLP, OCR, generative AI, prompt engineering, and MLOps using cloud AI services. The role mentors data scientists, partners with stakeholders, communicates insights through storytelling, and applies expertise to banking, financial services, and insurance use cases.

Leads data science initiatives using statistical modeling, machine learning, NLP, OCR, and generative AI technologies. Responsibilities include feature engineering, hypothesis testing, model development, stakeholder engagement, analytical storytelling, structured problem solving, and people leadership focused on talent development and team engagement.

Develop and support enterprise AI applications under senior engineering guidance. Responsibilities include building AI components with Python or C#/.NET, retrieval-augmented workflows, conversational Microsoft Teams experiences, REST integrations, deployments, infrastructure automation, model and prompt validation, observability, documentation, and Agile collaboration. The role emphasizes responsible AI, security, privacy, human-in-the-loop design, and learning established engineering practices.

Designs and delivers enterprise data and AI capabilities from concept through production. Responsibilities include defining target architectures, evaluating build-versus-buy decisions, building hands-on data and AI solutions, reviewing code and models, guiding platform adoption, and advising executive stakeholders. The role covers machine learning, generative AI, RAG, agent architectures, semantic layers, MLOps, model governance, cloud data platforms, and production readiness in a regulated environment.

Lead enterprise-scale adoption of AI tools by embedding with teams, identifying workflow opportunities, co-developing prompts, agents, and AI applications, and scaling successful solutions. Partner with Technology, Data, Risk, and strategy teams to ensure secure and compliant deployment. Track adoption, efficiency, and growth metrics; communicate outcomes to executives; and champion AI fluency and experimentation across the organization.

Leads enterprise AI-first technology delivery transformation at MetLife. Defines operating models, roadmaps, governance, controls, adoption strategies, delivery metrics, and readiness practices for agentic software development. Partners with CIO organizations, DevSecOps, Platform Engineering, Architecture, Risk, Audit, and Legal to implement traceable, reviewable AI-assisted workflows in a regulated environment. Advises senior leaders, resolves cross-organizational ambiguity, and drives initiatives through measurable adoption and business value realization.

Lead execution of Responsible Use of AI (RUAI) approvals and AI risk oversight. Coordinate cross-functional reviews, assess and document AI risks, improve governance tools/workflows, and escalate material issues to leadership. Engage use case owners, vendors, and control partners to ensure compliance with AI regulatory and control frameworks.

Lead design and governance of AI-enabled development platforms and agent frameworks for citizen and associate developers. Define reference architectures, reusable patterns, guardrails, and embedded controls for security, compliance, explainability, and resiliency. Partner with engineering, security, and platform teams to validate solutions, drive adoption, and enable reuse through templates, components, and training.

Lead Data, Analytics, and AI strategy and delivery to drive enterprise transformation. Oversee end-to-end ML/AI product lifecycle, ensure responsible deployment and regulatory compliance, build and coach high-performing teams, collaborate with cross-functional and executive stakeholders, and institutionalize best practices and reusable frameworks to accelerate production-grade AI solutions.