- Build and scale the infrastructure that powers real-time AI inference across GPU fleets, bare-metal servers, serverless and containerised production systems - Help evolve Runware’s platform toward more elastic, on-demand infrastructure that can scale quickly with customer traffic and model demand - Make Runware faster, more reliable and more resilient by improving the critical paths behind our request entrypoints, inference services, queues, storage, load balancers and networking layer - Automate the hard parts of infrastructure operations, from provisioning and configuration through to CI/CD, deployment safety, progressive rollouts and rapid rollback - Build the observability backbone for a high-performance AI platform, with the signals needed to spot issues early, understand capacity and fix problems before customers feel them - Play a leading role in production operations, incident response, debugging and post-incident improvements, helping us turn operational challenges into a stronger platform - Strengthen the security and compliance foundations of our infrastructure through patching, secrets management, access controls, hardening, auditability, documentation and repeatable operational processes

• Operate and improve multi-region GKE clusters hosting hundreds of microservices across multiple environments from development through production • Manage the Kubernetes platform layer: Istio service mesh, cert-manager, external-dns, RBAC, HPA/KEDA autoscaling, HashiCorp Vault secret injection, and Helm-based deployments • Develop and maintain Terraform modules across multiple IaC repositories covering GKE, networking (Shared VPC, Cloud NAT, Private Service Connect), Cloud SQL, Cloud Storage, Dataproc, Cloud Composer, Vault, and web hosting • Maintain and extend Azure DevOps CI/CD pipelines using shared Terraform templates with multi-environment deployment workflows • Support Confluent Kafka infrastructure including Connect workers with JDBC source connectors, consumer group health monitoring, and Kafka-lag-based autoscaling with KEDA • Manage Redis Enterprise clusters on Kubernetes with operator-managed lifecycle and replication • Operate the observability stack: Grafana Cloud (Alloy, Loki, Mimir, Tempo, Pyroscope via Private Service Connect), kube-prometheus-stack, Google Managed Prometheus, OpenTelemetry Operator/Collector, Beyla, and Kubecost • Harden cluster security posture: NetworkPolicies, Pod Security Standards, admission policy enforcement, CrowdStrike Falcon, Lacework, kube-bench, and cert-manager with Let’s Encrypt ACME • Support data infrastructure including Cloud SQL (PostgreSQL), Dataproc (Spark), Cloud Composer (Airflow), Matillion CDC pipelines, Snowflake, and BigQuery • Manage DNS across multiple providers (Azure DNS, Cloudflare, GCP Cloud DNS) via external-dns, and support Azure APIM and Cloudflare CDN/WAF • Partner directly with application development teams to troubleshoot deployment failures, tune resource limits and autoscaling, and resolve Kafka consumer lag and connectivity issues • Contribute to the Internal Developer Portal (Backstage) and internal CLI tooling that enables self-service for product engineers.

• Design, implement, and evolve GCP-based infrastructure using Infrastructure as Code with Terraform and Google Cloud deployment automation patterns. • Build and maintain scalable CI/CD pipelines using Cloud Build, GitHub Actions, Jenkins, or equivalent platforms for application, infrastructure, and platform workloads. • Administer and optimize GCP delivery workflows including Cloud Build triggers, Artifact Registry, source integrations, deployment approvals, and service account access patterns. • Partner with engineering teams to improve build, release, and deployment workflows across microservices and cloud-native applications. • Implement robust observability across systems using Google Cloud Operations Suite, Cloud Logging, Cloud Monitoring, and related telemetry tooling. • Strengthen platform security by integrating secrets management, policy enforcement, vulnerability scanning, and least-privilege access control. • Manage and optimize containerized environments using Kubernetes, Helm, and Google Kubernetes Engine (GKE). • Drive reliability engineering practices including incident response, root cause analysis, SLO thinking, and automated remediation where appropriate. • Standardize reusable templates, modules, and platform patterns that improve developer productivity and consistency. • Mentor engineers and provide technical leadership on GCP architecture, deployment automation, release governance, and DevSecOps practices.

Role Description We’re seeking a Mid-Level Cloud/DevOps Engineer to independently design, build, and operate cloud infrastructure and DevOps workflows on AWS and Azure. In this role, you will own key infrastructure components end to end, drive CI/CD maturity, manage containerized and serverless workloads, and collaborate with development and data teams to ensure reliable, secure, and cost-optimized cloud operations. You will also support cloud migrations, build automation frameworks, and integrate AI-powered tools into operational workflows. What You’ll Do - Design, provision, and manage production cloud infrastructure on AWS and Azure using Terraform, CloudFormation, CDK, Bicep, or Pulumi. - Build and maintain robust CI/CD pipelines for application and infrastructure deployments using GitHub Actions, GitLab CI, Jenkins, AWS CodePipeline, or Azure DevOps. - Deploy and manage containerized workloads on Amazon ECS, EKS, or Azure Kubernetes Service (AKS), including service mesh and auto-scaling configurations. - Implement serverless architectures using AWS Lambda, Step Functions, API Gateway, EventBridge, Azure Functions, and Logic Apps. - Support and execute cloud migration projects, ensuring scalability, security, and minimal downtime during transitions. - Develop and maintain Django-based and Python back-end applications integrated with cloud-native services such as RDS, DynamoDB, ElastiCache, S3, and SQS. - Implement infrastructure monitoring, alerting, and observability using CloudWatch, Azure Monitor, Prometheus, Grafana, Datadog, or OpenTelemetry. - Automate operational workflows using Python, Bash, Go, or PowerShell and build self-healing infrastructure patterns. - Enforce security best practices including IAM, VPC design, WAF, secrets management (Secrets Manager, Vault), encryption, and compliance controls. - Optimize cloud costs through reserved instances, savings plans, right-sizing, and FinOps practices. - Collaborate with development, data engineering, and ML teams to support application deployments, data pipelines, and AI/ML workloads. Qualifications - Bachelor’s or Master’s degree in Computer Science, Information Technology, or a related field. - 3–5 years of professional experience in cloud engineering, DevOps, or site reliability engineering with production responsibility. - Strong hands-on experience with AWS services (EC2, S3, RDS, Lambda, VPC, ECS/EKS, CloudFormation, IAM) and/or Azure services (VMs, AKS, App Service, Functions, ARM/Bicep). - Proficiency with infrastructure-as-code tools: Terraform (strongly preferred), CloudFormation, CDK, or Pulumi. - Experience building and managing CI/CD pipelines with GitHub Actions, GitLab CI, Jenkins, or cloud-native pipeline services. - Working knowledge of Docker, Kubernetes, and container orchestration in production environments. - Strong scripting skills in Python, Bash, or Go for automation and tooling. - Solid understanding of networking (VPC, subnets, load balancers, DNS, CDN), security (IAM, RBAC, encryption), and compliance in cloud environments. - Experience with monitoring and observability tools: CloudWatch, Prometheus, Grafana, Datadog, or equivalent. - Familiarity with Agile methodologies and collaborative development workflows. Preferred Qualifications - AWS Solutions Architect Associate/Professional, AWS DevOps Engineer Professional, or AZ-104/AZ-400 certification. - Experience with Django or FastAPI application deployment on cloud infrastructure. - Familiarity with GitOps practices and tools (ArgoCD, Flux). - Experience with database management on cloud platforms: RDS, Aurora, DynamoDB, Azure SQL, Cosmos DB. - Exposure to AI/ML infrastructure: SageMaker, Azure ML, Bedrock, GPU instance management, or model serving pipelines. - Experience with event-driven architectures using Kafka, SQS/SNS, EventBridge, or Kinesis. - Knowledge of FinOps principles and cloud cost optimization strategies. Benefits - Internet allowance - Laptop - PF - Paid PTO - Annual Bonus - Graduity - Yearly Team building experiences - Mentorship and sponsorship opportunities - Manager resources and support - Life & accidental insurance for additional protection.