Senior AI Platform Engineer / Senior DevOps and AI Platform Engineer

Senior AI Platform Engineer

Key Responsibilities

• Platform as a Product:

  Define platform value propositions, roadmaps, SLAs, and feedback loops; treat internal developers as customers.

• Golden Paths & Self-Service:

  Build reusable templates and paved paths for common developer tasks such as deploy, provision, observe, and rollback.

• Toolchain Integration:

  Seamlessly integrate CI/CD, secrets management, policy, observability, and environment automation into the IDP.

• Security by Default:

  Collaborate with security teams to embed guardrails and identity controls into the platform.

• Reliability & Cost Ownership:

  Own platform SLOs, performance, and FinOps hygiene; continuously optimize stability and spend.

• Adoption & Enablement:

  Lead onboarding, documentation, training workshops, and change management initiatives.

Required Experience

• 7-10+ years in DevOps / Platform Engineering / SRE roles.
  
• Minimum 3-4 years hands-on experience in building and operating IDPs or equivalent developer platforms.
  
• Proven experience in building developer abstractions like templates, CLIs, APIs, or internal portals.
  
• Experience leading cross-functional technical initiatives and mentoring junior engineers.
• Experience in AWS or GCP
  

Technical Skills Must Have

1. Cloud & Runtime

   : Strong hands-on experience with AWS or GCP, managing Kubernetes (multi-tenant clusters, autoscaling, admission controllers), Docker/OCI, GPU-aware environments (e.g., NVIDIA GPU Operator, CUDA drivers), and artifact registries.

2. Infrastructure as Code & Configuration

   : Deep expertise in Terraform (modules, workspaces), Helm/Kustomize, and config tools like Ansible for reliable, scalable provisioning and environment automation.

3. CI/CD, GitOps & Observability

   : Proven ability to build robust pipelines using GitHub Actions, GitLab CI, Jenkins, or Argo CD, with support for blue-green/canary deployments, rollbacks, and observability tools like Prometheus, Grafana, OpenTelemetry, and centralized logging (ELK/EFK).

4. AI/ML Platform Enablement

   : Knowledge or experience supporting AI/ML workflows, including tools like MLflow, Vertex AI, Kubeflow, model registries, feature stores, and orchestration tools like Airflow or Argo Workflows. Familiarity with data versioning (DVC, lakeFS) and reproducible training pipelines.

5. Security, FinOps & Developer Experience

   : Strong knowledge of OIDC/OAuth2, ABAC, secrets and policy integration, FinOps best practices (compute/GPU/storage optimization), and commitment to developer enablement (onboarding, documentation, feedback loops, secure-by-default patterns).