Senior DevOps Engineer

📋 Description

• Assist in transitioning the cloud platform from pre-production to a state of production readiness and scalability.

• Collaborate closely with engineering and data teams to implement infrastructure as code.

• Enhance deployment pipelines, establish monitoring and alerting systems, and facilitate the production deployment of data pipelines and risk oracle workloads.

• Evaluate and strengthen the existing platform setup, primarily on GCP.

• Transition infrastructure to Infrastructure-as-Code using Terraform or similar tools.

• Standardize development, staging, and production environments.

• Design and manage the platform's networking layer, including VPC architecture, private connectivity, and load balancing.

• Guide decisions regarding workload management between Cloud Run and GKE.

• Secure GitHub Actions pipelines.

• Implement monitoring, logging, tracing, alerting, and dashboards throughout the platform.

• Collaborate with data teams to productionize data pipelines and risk oracle workloads.

• Set up secrets management, audit logging, IAM, and access patterns.

⛳️ Requirements

• A minimum of 5 years of experience in DevOps, Platform, or SRE roles, with at least 2 years focused on GCP; familiarity with Vertex AI, AWS, or Azure is advantageous.

• Practical experience with Infrastructure-as-Code tools such as Terraform, Pulumi, CDK, or similar.

• Extensive CI/CD experience, particularly with GitHub Actions or comparable platforms.

• Experience in deploying and managing containerized services using Cloud Run, Kubernetes/GKE, ECS, or similar technologies.

• Sound judgment on when to opt for managed or serverless platforms versus Kubernetes or orchestrated methods.

• Experience in managing production data and caching infrastructure, including Cloud SQL/Postgres, Redis/Memorystore.

• Proficient in establishing production monitoring, logging, alerting, dashboards, and reliability targets.

• Strong understanding of cloud security principles, including IAM, secrets management, and audit logging.

• Familiarity with workflow orchestration or asynchronous task systems such as Temporal, Celery, or similar.

• Experience in supporting ML or AI inference workloads in a production environment, with hands-on experience across vector databases.

🏝️ Benefits

• Engage in global projects with clients from around the world.

• Be part of a remote-first culture, allowing flexibility to work from anywhere.

• Participate in team-building activities and regular outings.

• Collaborate and develop in a nurturing environment with opportunities to learn from experienced engineers.

• Enjoy a competitive salary and benefits package.