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AI Platform Engineer | AI Platform Engineering

Full-Time

Hybrid Onsite (3 days/week)

MassMutual's AI Platform Engineering team is looking for a curious, motivated AI Platform Engineer to launch their engineering career on a high-performing team. This is an entry-level role built for recent graduates and early-career engineers. You will learn directly from experienced platform engineers, contribute to real initiatives from your first weeks, and steadily build the skills to help design, deploy, and operate the systems that power AI across the enterprise. We care less about everything you already know and more about how quickly you learn, how you approach problems, and how much you care about doing good engineering work.

This is a unique opportunity to join the team that builds and operates the AI platform powering MassMutual's AI initiatives. The team works at the intersection of cloud infrastructure, AI/ML systems, and developer experience—delivering the 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, mentorship, and shared technical standards. It is a team where early-career engineers are set up to succeed: hard problems are made tractable through clear documentation, thoughtful onboarding, and engineers who genuinely enjoy teaching.

• Contribute to platform components—cloud infrastructure, AI serving layers, and developer tooling—under the guidance of senior engineers, growing your understanding of how the pieces fit together.
• Support the design and implementation of platform features such as the LLM gateway, model serving infrastructure, and integration patterns—writing code, tests, and documentation with regular feedback from your team.
• Learn the team’s engineering standards by participating in design reviews and code reviews, and by pairing with more experienced engineers on real problems.
• Take ownership of well-scoped tasks within larger initiatives, delivering them to production with support and steadily taking on more scope over time.
• Help keep the platform healthy by learning reliability practices—monitoring, alerting, SLOs, and incident reviews—and pitching in on operational work.
• Build familiarity with governance and compliance concepts such as access management, audit logging, and AI usage policies, and why they matter to enterprise customers.
• Communicate clearly and ask good questions—sharing what you learn, flagging blockers early, and collaborating with teammates and partner teams.
• Invest in your own growth through mentorship, pairing, and continuous learning, with the goal of ramping toward greater technical independence.

• Bachelor’s degree in Computer Science, Software Engineering, or a related technical field
• Foundational understanding of cloud computing and exposure to at least one major cloud provider (AWS, GCP, or Azure) through coursework, labs, or projects as shown by coursework or certification.
• 2+ years experience in programming proficiency in at least one language such as Python, Go, Java, or a comparable language (this could include coursework, internship experience, bootcamp, etc).

• Professional experience preferred: Internships, co-ops, apprenticeships, academic projects, and substantial personal projects all count.
• Basic familiarity with version control (Git) and a willingness to learn CI/CD, containers (Docker/Kubernetes), and infrastructure-as-code.
• Curiosity about AI/ML systems and an interest in how models are deployed and served in production.
• Strong written and verbal communication and a genuine eagerness to learn from feedback.
• Hands-on exposure to Kubernetes, Docker, or Terraform through coursework, certifications, hackathons, or personal projects.
• Relevant entry-level certifications are a plus but not required—for example AWS Certified Cloud Practitioner, AWS Solutions Architect – Associate, or CKAD.
• Any hands-on experience with AI/ML frameworks or LLM APIs, even at a hobby or class-project level.
• A public portfolio or open-source contributions (e.g., GitHub) that show initiative and a habit of building.
• Experience collaborating on a team project such as a capstone, hackathon, or group assignment.
• Comfort with ambiguity and enthusiasm for learning quickly in a fast-moving space.

• Structured onboarding and a dedicated mentor to support your ramp-up and early growth
• 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
• A company with a strong and stable ethical business, industry-leading pay and benefits, where your ethics and integrity will be valued

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