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.

• Contributing to long-term initiatives such as, but not limited to: Unlocking on-premises deployment of space algorithms with Kubernetes • Continuously improving the developer experience of your teammates (software and space) • Collaborating with space engineers and other software engineers to develop algorithms as services • Designing and developing core algorithms and infrastructure from early-stage prototypes to final deployment • Enhancing and extending the observability of our systems • Reviewing PRs and design documents, contributing to code via business-driven implementations and bug fixes, and reducing tech debt

Role Description Runware is building the API layer for the next generation of AI products. Our platform gives teams fast, reliable access to real-time inference across thousands of models through a single flexible API. We help customers build and scale media generation products with better performance, lower cost, and less operational complexity. We are looking for a Staff/Senior DevOps Engineer to help build, operate, and scale the infrastructure behind Runware’s global AI inference platform. You’ll play a critical role in making our systems faster, more resilient, easier to operate, and ready for the next stage of growth. As a Staff/Senior DevOps Engineer, you’ll help design, build, and operate the systems that power real-time AI inference across large-scale GPU fleets and a global production platform. This is not a traditional DevOps role; you’ll be working at the intersection of: - Bare-metal infrastructure - GPUs - Networking - Automation - Observability - High-performance distributed systems You’ll turn complex, hardware-driven infrastructure into reliable, automated, developer-friendly platforms. Your responsibilities will include: - Provisioning and orchestration - Deployment pipelines - Monitoring - Incident response - Capacity scaling You’ll build the foundations that let Runware scale with confidence: infrastructure that is fast, resilient, observable, secure, and built for the demands of real-time AI. Qualifications - Strong experience as a DevOps Engineer, SRE, Infrastructure Engineer, Platform Engineer or similar - Deep Linux knowledge and confidence debugging real production issues across networking, storage, performance, services and system behaviour - Hands-on experience building automation, Infrastructure-as-Code, CI/CD pipelines and deployment workflows - Experience operating high-availability, low-latency or high-throughput platforms - Strong networking fundamentals across TCP/IP, DNS, load balancing, routing, firewalls, proxies, TLS and HTTP - A calm and pragmatic approach under pressure - Strong communication skills and good judgement - Bias toward automation over manual toil Requirements - 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 - Make Runware faster, more reliable and more resilient by improving critical paths - Automate the hard parts of infrastructure operations - Build the observability backbone for a high-performance AI platform - Play a leading role in production operations, incident response, debugging and post-incident improvements - Strengthen the security and compliance foundations of our infrastructure Benefits - Generous paid time off – vacation, sick days, public holidays - Meaningful stock options – share in the upside you create - Remote-first setup – work from home anywhere we can employ you - Flexible hours – own your schedule outside core collaboration blocks - Family leave – paid maternity, paternity, and caregiver time - Company retreats – twice-yearly gatherings in inspiring locations

• Diseñar, construir y operar infraestructuras cloud nativas en AWS. • Desarrollar y mantener servicios backend y APIs en Node.js. • Crear y mejorar pipelines CI/CD y despliegues automatizados. • Integrar prácticas DevOps para mejorar fiabilidad, seguridad y rendimiento. • Mejorar la observabilidad, resolución de incidencias y salud del sistema. • Colaborar con equipos de producto y arquitectura en un entorno LeSS.