Staff AI Infrastructure / Platform Engineer (IT)

Staff AI Platform Engineer 

About the Role 

We're building a new AI Platform team, and we're looking for a mid-to-senior engineer to help shape it. This team is the backbone that lets the rest of the company build with AI safely, reliably, and cost-effectively. We own the MLOps and LLMOps tooling, and we're the bridge to the cloud model providers we depend on (AWS Bedrock, Google Vertex AI, and others). 

You'll work alongside senior engineers who set the architectural direction, and you'll be trusted to own significant pieces of the platform yourself building the reusable components, APIs, and integration patterns that accelerate AI adoption across the enterprise. This is a hands-on platform and operations role, not a research or model-training role: your job is to make it easy, reliable, and cost-effective for other teams to build with AI. 

This is a great role for a strong platform or backend engineer who is cloud-comfortable, thinks about reliability and cost, and is genuinely curious about the fast-moving AI space. 

What You'll Do 

• Build and operate the platform and tooling our teams use to develop, deploy, and monitor AI-powered applications. 

• Create platform enablers — reusable components, APIs, and integration patterns — that make it faster and safer for teams to adopt AI. 

• Own our integrations with cloud model providers (AWS Bedrock, Google Vertex AI), including usage, cost, and reliability. 

• Be a key point of contact for internal users bringing us AI use cases — understand what they're asking for, ask the right questions, and route them to the right approach. 

• Drive observability: make sure we can see what our systems are doing, and debug them when they break (logging, metrics, tracing). 

• Contribute to and uphold AI engineering standards — security, compliance, cost, and governance — across our multi-cloud environment. 

• Grow into deeper MLOps/LLMOps ownership over time, and help level up the practices of the team as it matures. 

Must-Have Skills 

• Solid software engineering fundamentals with strong, working Python — you write clean, maintainable code, use Git, and are comfortable automating tasks. 

• Hands-on experience with at least one major cloud provider (AWS is a plus given our stack, but strong GCP/Azure experience is welcome — good engineers pick up a second cloud quickly). 

• Working knowledge of cloud fundamentals: IAM/permissions, networking basics, and a healthy awareness of cost (GPU instances and model API calls add up fast). 

• Docker and comfort working with containerized applications. 

• A strong operations and reliability mindset — you care about monitoring, reproducibility, logging, metrics, and tracing. 

• Experience building or operating platforms, internal tooling, or services that other engineers depend on. 

• Strong communication and a service mindset. A platform team exists to support other engineers, so being approachable and asking good questions matters as much as technical skill. 

• A demonstrated ability to learn fast. This field reinvents its tooling constantly; curiosity beats a fixed checklist of tools. 

Foundational Knowledge 

You won't be building these from scratch but you should understand them well enough to have an intelligent conversation and point people in the right direction: 

• The LLM landscape: what it means to call a hosted model via a gateway like Bedrock or Vertex AI, and the basics of tokens, cost, latency, and context limits. 

• RAG (retrieval-augmented generation): what it is and why someone would use it to ground a model in their own data. You'll recognize when a user is describing one. 

• Fine-tuning: understanding that it's a training process that needs GPU compute (e.g., AWS EC2 GPU instances), and that it's a heavier lift than prompting or RAG. The key skill is recognizing what a user's request implies for resources and cost. 

• Agentic AI: a working understanding of reasoning, planning, and autonomous workflows, and the orchestration frameworks (e.g., LangChain) that support them. 

Nice to Have 

• Exposure to CI/CD (GitHub Actions, GitLab CI, or similar). 

• Exposure to Infrastructure as Code (Terraform, Pulumi, CloudFormation). 

• Kubernetes, or a strong desire to learn it. 

• Familiarity with the broader model provider ecosystem (OpenAI, Anthropic, Hugging Face, etc.). 

• Hands-on exposure to ML/LLM tooling or agentic systems — a genuine bonus, not an expectation. 

• A background in distributed systems, scalability, or performance optimization for demanding workloads. 

Preferred Education and Experience 

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field (or equivalent practical experience). 

• 4-6 years of platform engineering experience, ideally including cloud-native infrastructure, backend services, or internal developer platforms. 

• Experience delivering and operating production systems in cloud environments, with an eye to security and reliability. 

• Exposure to the AI/ML or MLOps/LLMOps space is a strong plus, but we're happy to teach the depth to a strong platform engineer who's eager to grow into it.