Senior 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).