Senior Platform Engineer

Responsibilities

• Set technical direction for the platform: drive architecture decisions, write ADRs, evaluate trade‑offs, and bring the team along.
• Design, build, and operate platform services on GCP, covering compute (GKE, Cloud Run, Compute Engine), networking, IAM, secrets, observability, and CI/CD integrations.
• Author and maintain Terraform modules and a clean state strategy across multiple GCP projects and environments.
• Import, refactor, and clean up legacy infrastructure when needed.
• Build and operate GKE clusters: workload identity, networking, ingress (nginx), autoscaling, upgrades, policy, and cost controls.
• Stand up serverless and PaaS patterns for application teams (Cloud Run, Cloud Functions, Pub/Sub, Workflows) with sensible defaults baked in.
• Partner with AI / Software Engineers to operationalize agents and models:
  Vertex AI Agent Engine deployments, model endpoints, evaluation and observability pipelines, prompt/secret management, and CI/CD for AI artifacts.
• Implement secure‑by‑default CI/CD with Git Lab using Workload Identity Federation: no long‑lived keys, least‑privilege service accounts, reproducible deploys.
• Contribute to Azure and AWS work as needed: networking, identity, storage, and align patterns across clouds.
• Improve developer experience: golden paths, paved roads, internal documentation, and templates that make the right thing the easy thing.
• Mentor other engineers on the team through code review, pairing, design feedback, and knowledge‑sharing.
• Raise the technical bar across Cloud Ops.
• Lead incident response and post‑incident reviews for systems you own and help mature how the team operates on call.

• 7+ years of hands‑on cloud engineering or platform/SRE experience, with at least 3 years primarily in GCP.
• Demonstrated technical leadership: setting architecture direction, driving cross‑team decisions, and mentoring other engineers.
• Expert‑level Terraform skills: module design, state management at scale, refactoring real‑world (messy) infrastructure, and comfort importing orphaned resources.
• Deep production Kubernetes experience: GKE preferred, including workload identity, RBAC, ingress controllers (nginx), Helm, cluster upgrades and operations, and policy/admission controls.
• Solid grasp of serverless and PaaS patterns (Cloud Run, Cloud Functions, or equivalents) and a strong point of view on when to choose them over containers or VMs.
• Working knowledge of IAM done right: least‑privilege design, workload identity federation, service account hygiene, and break‑glass patterns.
• CI/CD experience (Git Lab, Git Hub Actions, or equivalent) including keyless auth to cloud providers and pipeline design for multi‑environment promotion.
• Proficient in Python and/or Go and shell for tooling, automation, and glue code.
• Understanding of how Platform Engineering supports AI operationalization: model and agent deployment patterns, secrets and credentials for LLM/model APIs, observability for non‑deterministic systems, and the difference between "demo working" and "production ready" for AI workloads.
• Strong written and verbal communication skills: comfortable writing design docs, leading reviews, and influencing engineers outside your immediate team.
• Nice to Have:
  Hands‑on experience with Vertex AI, Agent Engine, or other model‑serving platforms.
• Experience operating multi‑cloud or hybrid environments (GCP + Azure + AWS).
• Background working alongside Fin Ops to drive cloud cost visibility and accountability.
• Experience contributing to or building internal developer platforms (Backstage, custom portals, Port.io, etc.).
• Google Cloud Professional Cloud Architect
• Google Cloud Professional Dev Ops Engineer
• AWS Certified Solutions Architect (Associate or Professional)
• AWS Certified Dev Ops Engineer, Professional
• Microsoft Certified:
  Azure Solutions Architect Expert
• Microsoft Certified:
  Dev Ops Engineer Expert
• CKA / CKAD / CKS (Kubernetes certifications)
• Hashi Corp Certified:
  Terraform Associate