Lead AI Platform Engineer

About the role

MassMutual is looking for an impact‑driven Lead AI Platform Engineer to embed with IT portfolio teams and deliver AI‑enabled solutions across the full software development lifecycle. You will combine enterprise‑level strategy with hands‑on implementation to improve build and run efficiency while embedding security and responsible AI practices.

Key responsibilities

• Partner with IT teams to diagnose engineering effort, design and deliver AI‑powered workflows, code generation, test automation, CI/CD enhancements, and legacy modernization.
• Build and ship integrations, evaluation harnesses, and automation agents, then package them as reusable accelerators, playbooks, and reference implementations.
• Embed security and responsible AI controls, including threat modeling, secure pipelines, secrets management, software‑supply‑chain integrity, audit logging, and human‑in‑the‑loop safeguards.
• Define AI‑enabled engineering strategy, assess workflows, prioritize opportunities, and create a sequenced roadmap with measurable business cases.
• Establish baselines, track efficiency metrics (cycle time, deployment frequency, incident volume, cost), and report outcomes to senior leadership.

Required profile

• 12+ years of experience in software engineering, DevOps/platform engineering, SRE, security, or solutions engineering, including 5+ years in a lead or principal role with enterprise‑wide scope.
• Proven record of delivering measurable build and run efficiency gains in large, complex IT environments.
• Forward‑deployed or customer‑facing experience embedding with teams, running discovery, and shaping ambiguous problems into working solutions.
• Hands‑on experience applying LLMs and agentic AI to software delivery and operations, including prompt and context engineering.
• Strong communication skills for design docs, ADRs, and executive‑level presentations.

Required skills

• Large language models (LLMs), agentic AI, prompt engineering, AI‑assisted development, code review, test generation, workflow automation.
• DevSecOps practices: CI/CD, IaC, GitOps, automated testing, observability, incident and change management.
• Security tooling: static/dynamic analysis, dependency scanning, secrets management, SBOM generation.
• Cloud‑native architecture: containers, Kubernetes, managed cloud and AI services, model APIs, identity, networking.