Lead AI Platform Engineer

The Opportunity

MassMutual’s AI Platform Engineering team is seeking an impact-driven AI Platform Engineer, to apply the AI platform where it matters most: improving how MassMutual builds and runs technology. Embedded directly with IT teams across the portfolio, you will diagnose where engineering and operational effort goes, design and hands-on deliver AI-enabled solutions across the full software development lifecycle, including cybersecurity, and prove the results with data. You will pair enterprise-level strategy with deep implementation skill, turning what you learn in the field into reusable patterns and platform capabilities.

The Team

This is a unique opportunity to work on the team that builds and operates the platform powering MassMutual’s AI initiatives. The team operates at the intersection of cloud infrastructure, AI/ML systems, and developer experience—delivering foundational capabilities that shape how the entire organization builds and deploys AI. We partner closely with AI engineering, product, and cloud engineering teams across the enterprise, and we invest in growth through a culture of peer learning, candid feedback, and shared technical standards. This team is defined by a shared commitment to engineering excellence, clear documentation, and the kind of technical leadership that makes hard problems tractable.

The Impact

• Embed with IT portfolio teams and deliver outcomes hands-on—work shoulder to shoulder with engineering and operations teams, moving quickly from discovery to working solution to production, in their real environments, toolchains, and constraints.

• Apply the AI platform across the full SDLC—requirements and design, code generation and review, test automation, CI/CD, release, legacy modernization, and production support—to measurably improve build and run efficiency.

• Build and ship the hard parts yourself—agentic workflows, integrations with existing engineering and operations toolchains, evaluation harnesses, and automation—then package successes into reusable accelerators, playbooks, and reference implementations other teams can adopt.

• Embed security and responsible AI into every engagement—threat modeling for AI use cases, secure pipelines, access and secrets management, software supply chain integrity, audit logging, and human-in-the-loop controls. Translate governance and compliance requirements (e.g., NIST AI RMF, OWASP LLM Top 10) into practical controls teams can adopt without slowing delivery.

• Define the AI-enabled engineering strategy for the IT portfolio—assess workflows across teams, prioritize opportunities by measurable value, and translate them into a sequenced roadmap with clear business cases.

• Establish baselines and measure results—define build and run efficiency metrics (e.g., cycle time, deployment frequency, quality, toil, incident volume, time to resolve, and cost), track adoption and return, and report outcomes to senior IT leadership.