AI Platform Engineer

Salary: $105,000 - 149,000 per year Requirements:

• We require at least 2 years of experience in platform, infrastructure, or SRE roles, with proven ownership of platform services or components.
• We require at least 2 years of experience with cloud-native architecture across AWS, GCP, or Azure, including containerized deployments, managed services, and basic multi-tenancy approaches.
• We require at least 2 years of experience delivering platform capabilities from development through production, with the ability to handle ambiguity within a defined scope.
• We require at least 2 years of experience using infrastructure-as-code and GitOps tools such as Terraform or Pulumi and ArgoCD, along with standard deployment practices.
• We require a bachelors degree in Computer Science, Technology, Engineering, or Mathematics.
• Familiarity with Kubernetes and cloud-native concepts such as managed services, networking, and identity is preferred.
• Exposure to AI/ML infrastructure, including model serving, inference pipelines, or LLM integration patterns, is preferred.
• Strong written communication skills are preferred, including the ability to create useful technical documentation and explain implementation choices clearly.
• Hands-on experience with LLM serving frameworks such as vLLM, Triton, or Ray Serve, or knowledge of AI gateway patterns, is preferred.
• Exposure to FinOps principles or GPU cost management for inference workloads is preferred.
• Experience contributing to internal developer platforms with a focus on developer experience is preferred.
• Open-source contributions to platform or ML infrastructure tools are preferred.
• Ability to influence peers through technical credibility and collaborative problem-solving is preferred.
• Comfort working in ambiguous environments and asking clarifying questions to build shared understanding is preferred.

• We contribute to the design and implementation of platform components, including cloud infrastructure, AI serving layers, and developer tooling, with guidance from senior engineers.
• We take ownership of specific features or modules within critical platform systems such as the LLM gateway, model serving infrastructure, and enterprise integration patterns, while helping draft ADRs and technical documentation.
• We participate in design reviews, provide constructive feedback on pull requests, and collaborate with teammates on complex engineering problems.
• We deliver platform initiatives end to end within our scope by breaking down work, managing priorities, and shipping quality production outcomes.
• We support platform reliability by helping define SLOs, adding observability instrumentation, joining incident response, and improving stability.
• We implement governance and compliance controls, including data residency, access management, and audit logging, in line with enterprise standards.
• We work with AI engineering, product, and cloud engineering teams, and communicate technical context and trade-offs effectively in cross-functional discussions.
• We help strengthen team documentation practices, code quality, and shared engineering standards.

• AI
• AWS
• ArgoCD
• Azure
• Cloud
• GCP
• GitOps
• Support
• Kubernetes
• LLM
• Terraform

More:

We are MassMutuals AI Platform Engineering team, and we are seeking an AI Platform Engineer to help design, build, and operate the platform behind our AI initiatives. This is a full-time hybrid onsite role with three days per week in the office. Our team works across cloud infrastructure, AI/ML systems, and developer experience, partnering closely with AI engineering, product, and cloud engineering teams across the enterprise. We value engineering excellence, clear documentation, peer learning, candid feedback, and strong technical standards. You can expect regular team and manager check-ins, access to employee resource groups, learning resources through Degreed, and a company culture that emphasizes ethics, integrity, pay, and benefits.

last updated 36 week of 2026