DevOps Engineer

🕒 May 13

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Shuru

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2021

🤖 Artificial Intelligence

🤝 B2B

🏢 Enterprise

Artificial Intelligence • B2B • Enterprise

Shuru is a product, AI, and technology consulting firm that partners with businesses to deliver strategic consulting, full-cycle product and custom software development, and curated engineering team extension. Their AI-native engineering teams build scalable AI applications, data engineering and analytics, cloud/DevOps, and API integrations to modernize systems and accelerate product delivery. Shuru operates globally with a remote-first model and emphasizes high ownership, design thinking, and measurable outcomes for enterprise and startup clients.

📋 Description

• Help take cloud platform from pre-production to production readiness and scale. • Work closely with engineering and data teams to bring infrastructure under code, improve deployment pipelines, set up monitoring and alerting, and support production deployment of data pipelines and risk oracle workloads. • Assess and harden current platform setup, primarily on GCP, for production readiness. • Bring infrastructure under Infrastructure-as-Code using Terraform or similar. • Standardize development, staging, and production environments, including configuration, secrets, environment isolation, and deployment patterns. • Design and operate the platform's networking layer: VPC architecture, private connectivity, load balancing. • Lead decisions on when to use Cloud Run versus GKE or other orchestration approaches. • Harden GitHub Actions pipelines across various stages of deployment. • Set up monitoring, logging, tracing, alerting, and dashboards for quick issue diagnosis. • Establish secrets management, audit logging, IAM, and access patterns, with tested backup, restore, disaster recovery procedures. • Contribute to operational runbooks, incident response and postmortems, production readiness reviews.

🎯 Requirements

• 5+ years of DevOps, Platform, or SRE experience, ideally with at least 2 years working on GCP; experience with vertex ai , AWS or Azure is a plus. • Hands-on production experience with Infrastructure-as-Code tools such as Terraform, Pulumi, CDK, or similar, including managing separate development, staging, and production environments, and helping set up consistent local or individual developer environments. • Strong CI/CD experience, especially with GitHub Actions or similar, including build, test, release, rollback, and quality/security gates such as static analysis, dependency scanning, secret scanning, and container image scanning. • Experience deploying and operating containerized services using Cloud Run, Kubernetes/GKE, ECS, or similar platforms; comfortable writing and optimizing Dockerfiles (multi-stage builds, image hardening) and managing container registries (Artifact Registry, ECR, or similar). • Good judgment on when to use managed or serverless platforms versus Kubernetes or others, orchestrated approaches, balancing cost, scalability, reliability, operational complexity, and speed of delivery. • Experience operating production data and caching infrastructure, including Cloud SQL/Postgres, Redis/Memorystore, migrations, backup strategies, performance monitoring, and basic tuning. • Experience setting up production monitoring, logging, alerting, dashboards, and reliability targets using cloud-native monitoring, Sentry, Datadog, Grafana, Prometheus, or similar. • Solid understanding of cloud security fundamentals, including IAM, secrets management, audit logging, network controls, and backup/recovery. • Experience with workflow orchestration or async task systems such as Temporal, Celery or similar. • Experience supporting ML or AI inference workloads in production, with strong hands-on experience across vector databases (Weaviate a plus), retrieval infrastructure, AI application infrastructure, and managed AI platforms. Experience supporting workflow orchestration or async task systems in production, such as Temporal, Celery, or similar. • Exposure to model or agent deployment patterns, including real-time and background inference workflows, monitoring, evaluation workflows, and agent observability.

🏖️ Benefits

• Work on global projects with clients from worldwide. • Be part of a remote-first culture-work from anywhere with flexibility. • Enjoy team-building activities and regular outings. • Collaborate and grow in a supportive environment with opportunities to learn from senior engineers. • Competitive salary and benefits package.