Among computer and information research scientists, AI is already used for a large share of the tasks. Measured exposure for computer and information research scientists: 34% (high).

Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.

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About the job

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.

Serve as the senior technical escalation point for the most complex adoption and integration challenges across teams. Run design reviews, provide deep technical feedback, and pair with engineers on the gnarliest problems.

Close the loop with platform engineering—translate field learnings into prioritized capability requests, ADRs, and design input that improve the platform, without owning core platform build.

Drive adoption through influence—lead change with directors and engineering leaders across the portfolio, communicate technical trade-offs clearly to technical and executive audiences, and represent the platform to senior stakeholders.

Raise the technical bar across the organizationby mentoring leads and senior engineers, running enablement and communities of practice, and setting the standard for AI-assisted engineering—without managing anyone directly.

The Minimum Qualifications

12+ years total experience in software engineering, DevOps/platform engineering, SRE, security engineering, or solutions engineering, including 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: embedding with teams, running discovery, shaping ambiguous problems into working solutions, and driving adoption with teams you do not manage.

Hands-on experience applying LLMs and agentic AI to software delivery and operations—AI-assisted development, code review, test generation, workflow automation, and AI-enabled operations—including prompt and context engineering, evaluation, and integration with enterprise APIs.

Strong DevSecOps and SDLC fluency: CI/CD, IaC and GitOps, automated testing, observability, incident and change management, and secure-by-design practices (static, dynamic, and dependency scanning, secrets management, SBOM).

Working knowledge of cloud-native architecture and AI infrastructure—containers, Kubernetes, managed cloud and AI services, model APIs and gateways, identity, and networking—sufficient to integrate, troubleshoot, and advise. Deep experience building AI platforms from scratch is not required.

Experience designing security and governance controls for AI or data-sensitive systems in a regulated environment.

Ability to balance strategy and implementation: set direction at the portfolio level and personally build, debug, and ship.

Strong written and verbal communication: clear design docs and ADRs, and executive-level presentation skills for both engineers and non-technical stakeholders.

The Ideal Qualifications

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 time to resolve incidents.

Strong grasp of AI safety, model evaluation, red-teaming, and governance frameworks (e.g., NIST AI RMF, ISO/IEC 42001).

Developer-productivity and engineering-metrics experience (e.g., DORA, SPACE), with the ability to build a business case and demonstrate ROI.

FinOps or AI cost optimization experience across large AI workloads.

Ability to lead through technical credibility, influencing design decisions, driving alignment, and raising quality without formal authority.

Relevant certifications (cloud, Kubernetes, or security) and open-source contributions, publications, or conference talks.

Comfort with ambiguity: energized by undefined problem spaces, with a habit of building clarity and shared context where there isn’t any.

What to Expect as Part of MassMutual and the Team

Regular meetings with the AI Platform Engineering team

Focused one-on-one meetings with your manager

Networking opportunities including access to Asian, Hispanic/Latinx, African American, women, LGBTQIA+, veteran and disability-focused Business Resource Groups

Access to learning content on Degreed and other informational platforms

Your ethics and integrity will be valued by a company with a strong and stable ethical business with industry leading pay and benefits.

#LI-MC1 MassMutual is an equal employment opportunity employer. We welcome all persons to apply.

If you need an accommodation to complete the application process, please contact us and share the specifics of the assistance you need.

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