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.

Design and develop enterprise AI agents, multi-agent systems, LLM applications, document-processing solutions, and knowledge-retrieval systems. Build scalable microservices, APIs, data pipelines, and end-to-end integrations with backend systems, claims platforms, and workflow engines. Manage Azure DevOps CI/CD, production monitoring, support, and continuous improvements while ensuring performance, maintainability, governance, security, and compliance.

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.

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.

Design and implement production-grade AI and machine learning solutions on an enterprise AI platform. Collaborate with product, architecture, business, and platform teams to develop scalable solutions, provide technical guidance, improve platform capabilities, and ensure reliability, observability, quality, and responsible AI compliance. Mentor junior engineers and promote strong software engineering practices, including testing, documentation, and knowledge sharing.

Designs, develops, tests, deploys, and maintains secure, scalable cloud-native full-stack applications using AI-first engineering practices. Translates business outcomes into technical specifications, architecture decisions, and acceptance criteria. Uses AI coding tools responsibly, partners with product and technical teams, supports production incidents, and drives automation and continuous improvement. Requires modern full-stack, API, data, cloud, DevOps, CI/CD, automated testing, and secure software development experience, with preferred exposure to Azure AI Foundry, Semantic Kernel, AI agents, RAG, and LLM applications.

Lead and own the Data Science and AI function, driving analytics strategy, stakeholder engagement, portfolio outcomes, team leadership, technical design, and compliance with data policies to deliver measurable business value.

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.