Senior Platform Engineer (AI Focused)

About this role

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Skills you'll use

What you'll do

• Design and build MCP servers that expose platform capabilities as safe, well-scoped tools for AI agents and developer-facing assistants. Contribute to enterprise MCP patterns, LLM tooling, agentic guardrails, knowledge repositories, and framework rollout.
• Design structured, agentic workflows for platform operations — incident triage, deployment validation, config remediation, capacity planning — and drive AI-assisted code review and developer tooling (e.g., CodeRabbit, Claude/Cursor) with tool-use, validation steps, and human-in-the-loop gates.
• Operationalize LLM-based features inside platform tooling: structured prompting, RAG, output validation, and evaluation harnesses. Implement LLM gateway and router patterns to control costs, route models, and enable observability of AI workloads.
• Design and evolve GitOps-based continuous delivery, Kubernetes infrastructure on AWS EKS, and infrastructure-as-code with Terraform, Helm, and Kustomize.
• Strengthen observability, reliability, and operational excellence: SLOs, error budgets, metrics/traces/logs, and automation that improves MTTD/MTTR.
• Extend the developer control plane with paved paths, scorecards, and self-service actions.
• Instrument platform cost signals — compute, observability, and AI/LLM spend — and build FinOps automation that surfaces waste and supports cost-aware engineering decisions.
• Define success metrics upfront and run time-bound experiments to evaluate impact on developer efficiency, reliability, and cost.
• Document usage guidance, patterns, and best practices to support consistent adoption of proven AI workflows.
• WHAT WE'RE LOOKING FOR

Who can apply

• Document usage guidance, patterns, and best practices to support consistent adoption of proven AI workflows.
• Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
• 4+ years in platform, infrastructure, or backend engineering with hands-on experience operating production systems in a cloud environment (AWS preferred).
• Strong coding skills in Python and/or Go, with experience building and operating production services.
• Deep experience with Kubernetes (EKS preferred), GitOps (Argo CD), CI/CD (GitHub Actions), and Terraform/Helm/Kustomize for infrastructure automation, service mesh..
• Solid observability skills (Datadog APM/metrics/tracing/logs) with a track record of improving reliability and driving SLO/error-budget culture.
• Hands-on experience building or integrating MCP servers — designing tool surfaces, managing scope and auth, and connecting AI agents to real infrastructure.
• Practical experience with structured or agentic AI workflows (e.g., planning/execution separation, human-in-the-loop validation, RAG, tool-use patterns) used in production environments.

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