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GeoComply is a geolocation, cybersecurity, and anti-fraud technology company serving regulatory compliance and fraud prevention across global markets. The company operates compliance-grade geolocation solutions on over 400 million devices, analyzing 12+ billion transactions annually, and is currently navigating a critical infrastructure modernization phase.
As a Senior DevOps Engineer based in Ho Chi Minh City, you will be a hands-on technical leader responsible for designing, building, and operating the infrastructure and DevOps tooling that powers GeoComply's systems. You'll own significant workstreams across a major AWS-to-GCP migration and multi-region platform architecture, while mentoring engineers and partnering directly with development teams.
Key responsibilities include:
• Cloud Infrastructure & FinOps: Support and maintain AWS and GCP infrastructure for high availability, identify capability gaps, and apply FinOps practices to treat cost as a first-class design constraint.
• Multi-Cloud Delivery & Migration: Own and deliver workstreams across AWS and GCP, including the AWS-to-GCP migration using GCP-managed services (GKE, Cloud SQL, Spanner, Pub/Sub, BigQuery), while operating remaining business-critical AWS workloads.
• Multi-Region Architecture: Implement and operate multi-region infrastructure across AWS and GCP for low-latency traffic management and disaster recovery.
• Modernization & System Design: Support the transition from monolithic to microservices architecture, promoting automation, scalability, and maintainability.
• Security & Compliance: Embed security best practices (IAM, least privilege, secrets management, network policies, data protection) and ensure compliance with industry standards.
• Engineering Platform & Operational Excellence: Improve CI/CD, observability (LGTM stack, GCP Monitoring), SLIs/SLOs, on-call and incident response, and build reusable patterns and self-service tooling.
• Knowledge Sharing & Mentorship: Mentor teammates through code and architecture reviews; support development teams via reusable patterns and documentation.
• AI-Assisted Engineering: Leverage GenAI tools and AI agents to accelerate development and operational work, reviewing output against production standards.
The role is part of a global DevOps team working to reduce cloud platform dependency, optimize costs, ensure high availability and scalability, and modernize systems during a critical migration and innovation phase.
Requirements:
• 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.
• Production expertise with containerization, especially Kubernetes, including deep familiarity with cluster lifecycle management, scaling strategies, resource optimization, and troubleshooting.
• Proven experience designing modular Infrastructure as Code (e.g., Terraform) and building secure, automated CI/CD pipelines (e.g., GitHub Actions).
• Proven track record architecting and operating production environments on GCP and/or AWS, specifically leveraging managed services (GKE/EKS, Cloud SQL/RDS, Pub/Sub/SQS). Multi-cloud/multi-region experience is a plus.
• Direct ownership of highly available systems, including on-call, incident response, and post-incident improvement.
• Proven experience with telemetry pillars (metrics, logs, traces) and modern observability stacks (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 (Claude Code, OpenCode) on real DevOps work (IaC, debugging, incident triage, post-mortems).
• 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.
Bonus: Experience operating Service Mesh solutions (Istio, Linkerd) in complex microservices environments; ML Infrastructure Engineering experience.
