Lead AI Platform Engineer

MassMutual • Boston, MA

3d • $172,000 - $225,700

About The Position

MassMutual’s AI Platform Engineering team is seeking an impact-driven Lead AI Platform Engineer to apply the AI platform to improving how the company builds and runs technology. Embedded with IT teams across the portfolio, this role diagnoses where engineering and operational effort is spent, designs and delivers AI-enabled solutions across the software development lifecycle—including cybersecurity—and measures results. The role combines enterprise-level strategy with hands-on implementation, translating field learnings into reusable patterns and platform capabilities.

The AI Platform Engineering team builds and operates the platform powering MassMutual’s AI initiatives. It works at the intersection of cloud infrastructure, AI/ML systems, and developer experience, partnering with AI engineering, product, and cloud engineering teams across the enterprise. The team emphasizes engineering excellence, documentation, peer learning, candid feedback, and shared technical standards.

The Lead AI Platform Engineer embeds with IT portfolio teams to move from discovery to production solutions in their actual environments and toolchains. The role applies AI across the full SDLC, builds agentic workflows and integrations, establishes evaluation and automation practices, embeds security and responsible AI, defines engineering strategy and metrics, and serves as a senior technical escalation point. It also drives adoption through influence, mentors technical leaders, and channels field learnings into platform improvements. The position provides technical leadership without directly managing employees or owning core platform development.

The salary range is $172,000–$225,700. MassMutual states that total compensation includes a bonus target or, for sales-focused roles, a Variable Incentive Compensation component. The company describes its culture as inclusive, vibrant, diverse, and focused on growth, openness, customer priorities, and mutuality. MassMutual is an equal employment opportunity employer and offers accommodations during the application process.

Requirements

• 12+ years of total experience in software engineering, DevOps/platform engineering, SRE, security engineering, or solutions engineering.
• 5+ years at lead, staff, or principal level with multi-team or enterprise-wide technical scope.
• Proven record of delivering measurable build and run efficiency gains in large, complex IT environments, from strategy through hands-on implementation.
• Forward-deployed or customer-facing engineering experience, including embedding with teams, conducting discovery, shaping ambiguous problems into working solutions, and driving adoption with teams not directly managed.
• Hands-on experience applying LLMs and agentic AI to software delivery and operations, including AI-assisted development, code review, test generation, workflow automation, and AI-enabled operations.
• Experience with prompt and context engineering, evaluation, and integration with enterprise APIs.
• Strong DevSecOps and SDLC knowledge, including CI/CD, IaC and GitOps, automated testing, observability, incident and change management, and secure-by-design practices such as static, dynamic, and dependency scanning, secrets management, and SBOM.
• Working knowledge of cloud-native architecture and AI infrastructure, including containers, Kubernetes, managed cloud and AI services, model APIs and gateways, identity, and networking, sufficient to integrate, troubleshoot, and advise.
• Experience designing security and governance controls for AI or data-sensitive systems in a regulated environment.
• Ability to balance portfolio-level strategy with personally building, debugging, and shipping solutions.
• Strong written and verbal communication, including clear design documents and ADRs and executive-level presentations for technical and non-technical stakeholders.

Nice To Haves

• Prior forward-deployed, solutions engineering, customer success engineering, or internal consulting experience.
• Legacy modernization or large-scale migration experience using AI-assisted approaches.
• AIOps, observability, and IT service management automation experience that reduces toil and incident resolution time.
• Knowledge of AI safety, model evaluation, red-teaming, and governance frameworks such as NIST AI RMF and ISO/IEC 42001.
• Developer productivity and engineering metrics experience, such as DORA or SPACE, including building business cases and demonstrating ROI.
• FinOps or AI cost optimization experience across large AI workloads.
• Ability to lead through technical credibility, influence design decisions, drive alignment, and raise quality without formal authority.
• Relevant cloud, Kubernetes, or security certifications; open-source contributions, publications, or conference talks.
• Comfort with ambiguity and the ability to build clarity and shared context in undefined problem spaces.

Responsibilities

• Embed with IT portfolio teams and deliver hands-on outcomes, moving from discovery to working solutions and production in teams’ real environments, toolchains, and constraints.
• Apply the AI platform across requirements and design, code generation and review, test automation, CI/CD, release, legacy modernization, and production support to improve build and run efficiency.
• Build agentic workflows, integrations with engineering and operations toolchains, evaluation harnesses, and automation; package successful work into reusable accelerators, playbooks, and reference implementations.
• Embed security and responsible AI into engagements, including threat modeling, secure pipelines, access and secrets management, software supply chain integrity, audit logging, and human-in-the-loop controls.
• Translate governance and compliance requirements, including NIST AI RMF and OWASP LLM Top 10, into practical controls teams can adopt.
• Define AI-enabled engineering strategy for the IT portfolio by assessing workflows, prioritizing opportunities by measurable value, and creating a sequenced roadmap with business cases.
• Establish baselines and measure build and run efficiency, adoption, and return; report outcomes to senior IT leadership.
• Serve as the senior technical escalation point for complex adoption and integration challenges across teams.
• Run design reviews, provide deep technical feedback, and pair with engineers on complex technical problems.
• Translate field learnings into prioritized capability requests, ADRs, and design input for platform engineering, without owning core platform development.
• Drive adoption by influencing directors and engineering leaders, communicating technical trade-offs to technical and executive audiences, and representing the platform to senior stakeholders.
• Mentor leads and senior engineers, run enablement and communities of practice, and establish standards for AI-assisted engineering without direct people management.

Benefits

• Industry-leading pay and benefits, as described by the company.
• Incentive and bonus opportunities; total compensation includes either a bonus target or, for a sales-focused role, a Variable Incentive Compensation component.
• Access to learning content on Degreed and other informational platforms.
• Networking opportunities, including access to Business Resource Groups focused on Asian, Hispanic/Latinx, African American, women, LGBTQIA+, veteran, and disability communities.
• Regular meetings with the AI Platform Engineering team and focused one-on-one meetings with the manager.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed