Top AI & Machine Learning Jobs

Leads the design and implementation of MetLife’s AI-first delivery practice. Runs AI delivery pilots, develops context libraries and agentic coding patterns, establishes spec-driven development and review standards, creates reusable enablement assets, and enables teams across GitHub Enterprise and CI/CD tooling. Partners with DevSecOps, Platform Engineering, Architecture, and Learning & Development to address toolchain gaps, measure engineering effectiveness, build practitioner networks, and scale adoption in a governed enterprise environment.

Lead operations and talent development while aligning senior stakeholders across business and technology teams. Translate complex data science and AI concepts for business audiences, guide strategic problem-solving, and balance technical feasibility with business priorities. Requires understanding of the data science lifecycle, machine learning, predictive analytics, generative AI, cloud analytics, MLOps, governance, privacy, and responsible AI.

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.

Develops and deploys scalable data science, machine learning, Generative AI, and advanced analytics solutions. Responsibilities include SQL development, data preparation, feature engineering, predictive modeling, model evaluation, prompt engineering, embeddings, vector databases, RAG applications, monitoring, and responsible AI practices. The role collaborates with business and technology teams to deliver production-ready solutions and communicate actionable insights.

Leads end-to-end data science and advanced analytics initiatives, from problem definition and data analysis through machine learning development, testing, deployment, and delivery. Manages and coaches data scientists and vendors, reviews Python code, coordinates with platform and IT teams, and delivers cloud-based solutions on Azure. Communicates insights to business stakeholders and leaders while applying responsible AI and compliance practices.

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 AI Engineer responsible for converting business ideas into production AI-enabled products end-to-end: run discovery workshops, define MVPs, design cloud/agentic solutions, lead Python data pipelines and LLM orchestration, implement CI/CD and containerized deployments, ensure production handover, and coach/guide engineering teams while aligning stakeholders.