Systems Engineer - Cloud Ops
Cloud Infrastructure, Automation & Operations
Deploy, manage, and optimize cloud infrastructure using Terraform to automate provisioning, scaling, and lifecycle management of resources on GCP.
Develop and maintain CI/CD pipelines using GitLab CI to automate build, test, and deployment workflows.
Implement and maintain GitOps practices using ArgoCD for declarative, version-controlled application deployment.
Monitor system performance using observability tools (Dynatrace, Cloud Monitoring, Prometheus/Grafana) and troubleshoot production issues.
Document runbooks, architecture decisions, and operational procedures.
AI/ML Platform & Automation
Support infrastructure for AI/ML workloads including LLM-based applications and model serving platforms.
Deploy and manage AI-powered developer tools such as coding assistants (Claude Code, GitHub Copilot) and agentic AI systems.
Explore and implement AI-assisted incident response and automated remediation workflows.
Build and maintain infrastructure for Retrieval-Augmented Generation (RAG) pipelines and vector databases.
Configure GPU-enabled node pools and optimize resource allocation for AI/ML workloads.
Implement MCP (Model Context Protocol) servers and AI agent integrations for operational automation.
Stay current with emerging AI technologies and evaluate their applicability for infrastructure automation.
Kubernetes Platform Management
Deploy, configure, and manage containerized applications on Google Kubernetes Engine (GKE), including GKE Autopilot and Standard clusters.
Manage cluster lifecycle including upgrades, node pool configurations, and capacity planning.
Troubleshoot pod failures, CrashLoopBackOff, OOMKilled events, and container resource issues.
Configure and optimize resource requests/limits, Horizontal Pod Autoscaler (HPA), and Vertical Pod Autoscaler (VPA).
Manage Kubernetes networking including Services, Ingress controllers, Network Policies, and DNS configurations.
Implement and manage service mesh (Istio) for traffic management, observability, and security.
Implement pod security standards, RBAC policies, and workload identity configurations.
Observability & Troubleshooting
Experience with monitoring and APM tools (Dynatrace, Datadog, Prometheus, Grafana) to analyze logs, metrics, and traces to diagnose production issues.
Familiarity with JVM troubleshooting (heap dumps, thread analysis, GC tuning, connection pool issues).
AI/ML Knowledge
Basic understanding of LLM concepts, prompt engineering, and AI model deployment.
Familiarity with AI coding assistants and their integration into development workflows.
Interest in agentic AI systems and autonomous automation tools.
Exposure to vector databases (Pinecone, Weaviate, pgvector) and RAG architectures is a plus.
Systems & Networking
Strong Linux administration skills.
Understanding of networking concepts (DNS, load balancing, firewalls, TCP/IP).
Experience with service mesh (Istio).
General
Excellent problem-solving and analytical skills.
Strong written and verbal communication.
Ability to work effectively in a collaborative, cross-functional environment.
Experience working in an Agile/DevOps culture.
Bachelor's degree in Computer Science, Information Technology, or related field (or equivalent experience).