Cloud Infrastructure & FinOps: Support and maintain our AWS and GCP infrastructure for high availability, identify capability gaps within your scope and propose practical improvements, and treat cost as a first-class design constraint through FinOps practices.

Multi-Cloud Delivery & Migration: Own and deliver workstreams across AWS and GCP, including our AWS to GCP migration using GCP-managed services (e.g. GKE, Cloud SQL, Spanner, Pub/Sub, BigQuery), while continuing to operate the AWS workloads that remain business-critical, with cost, performance, and interoperability front of mind.

Multi-Region Architecture: Implement and operate multi-region infrastructure across AWS and GCP for low-latency traffic management (shadowing, shifting) and disaster recovery, in partnership with development and data teams.

Modernization & System Design: Support the transition from monoliths to microservices and align infrastructure with evolving application needs, promoting automation, scalability, and maintainability.

Security & Compliance: Embed security best practices across the infrastructure (IAM, least privilege, secrets management, network policies, data protection) and ensure compliance with industry standards and internal governance.

Engineering Platform & Operational Excellence: Improve CI/CD, observability (e.g. LGTM stack, GCP Monitoring), SLIs/SLOs, on-call and incident response, and post-mortems that reach systemic root cause; build reusable patterns and self-service tooling that shift DevOps from "Serve" to "Enable."

Knowledge Sharing & Mentorship: Mentor teammates through code and architecture reviews, and support development teams via reusable patterns, documentation, and self-service capabilities rather than by fielding their questions directly.

AI-Assisted Engineering: Leverage GenAI tools and AI agents to accelerate common development and operational work so your time goes to the highest-impact engineering, reviewing and verifying output against production standards for reliability, security, cost, and maintainability, and coach teammates to do the same.

5+ years in DevOps, cloud infrastructure, SRE, or platform engineering, with demonstrated experience taking medium-to-large infrastructure projects.

Strong analytical mindset to define complex problems, evaluate technical trade-offs, and independently navigate ambiguity to drive cross-system solutions to completion.

Production expertise with containerization especially in Kubernetes environment, including deep familiarity with cluster lifecycle management, scaling strategies, resource optimization, and troubleshooting cluster and workload issues.

Proven experience designing modular Infrastructure as Code (e.g. Terraform) and building secure, automated CI/CD pipelines (e.g. GitHub Actions).

Proven track record of architecting and operating production environments on GCP and/or AWS, specifically leveraging managed services (e.g. GKE/EKS, Cloud SQL/RDS, Pub/Sub/SQS). Experience with multi-cloud/multi-region is a plus.

Direct ownership of highly available systems, including on-call, incident response, and post-incident improvement.

Proven experience with pillars of telemetry (metrics, logs, traces) and modern observability stacks (e.g LGTM stack, Datadog). Practical experience with SRE concepts and managing cardinality, retention, and cost at scale.

Familiar and hands-on practice using AI coding assistants and agentic tools (e.g. Claude Code, OpenCode) on real DevOps work (IaC, debugging, incident triage, post-mortems).

Communication: Professional working proficiency in English, written and verbal. Must be able to explain technical decisions clearly in English, in writing, in design documents, and in live discussion.

Experience operating Service Mesh solutions (e.g. Istio, Linkerd) in complex microservices environments. Proven track record of tracing packet flow and optimizing end-to-end latency across multi-cluster, multi-region network topologies and cloud-managed service boundaries.

ML Infrastructure Engineering: Hands-on experience architecting and operating production ML platforms.