DevOps Engineer London, UK

We are seeking a talented and experienced DevOps Engineer to join our team. This engineer will contribute to the further development of Arena, a web-based software platform for reinforcement learning training and RLOps.

As a DevOps Engineer, you will be responsible for designing, implementing, and maintaining the cloud infrastructure, CI/CD pipelines, and deployment systems that enable businesses to build and deploy reinforcement learning models at scale.

Responsibilities

• Design and maintain robust, scalable cloud infrastructure to support high-performance reinforcement learning workloads and distributed training environments
• Build and optimise CI/CD pipelines for both our open-source framework and Arena enterprise platform, ensuring reliable deployments and automated testing
• Implement and manage containerisation strategies using Docker and Kubernetes for ML model training, deployment, and orchestration
• Develop infrastructure as code (IaC) solutions using tools like Terraform, CloudFormation, or Pulumi to ensure reproducible and version-controlled infrastructure
• Monitor system performance, implement alerting and logging solutions, and troubleshoot production issues across distributed ML training environments
• Collaborate with ML engineers to optimise resource allocation and cost efficiency for compute-intensive RL training workloads
• Implement security best practices, manage access controls, and ensure compliance with enterprise security requirements
• Automate operational tasks including backup strategies, disaster recovery procedures, and system maintenance
• Support the deployment and scaling of GPU clusters and distributed computing resources for reinforcement learning applications
• Maintain high availability and performance of production systems serving ML models to external customers

Requirements

• Bachelor's degree or higher in Computer Science, Engineering, or a related field, or 3+ years of relevant DevOps/infrastructure experience
• Strong experience with cloud platforms (AWS, GCP, Azure) and their ML/AI services, with expertise in managing compute-intensive workloads
• Proficiency in containerisation technologies (Docker, Kubernetes) and container orchestration for ML workloads
• Experience with Infrastructure as Code tools (Terraform, CloudFormation, Pulumi) and configuration management
• Solid understanding of CI/CD principles and tools (GitHub Actions, GitLab CI, Jenkins) with experience in ML pipeline automation
• Knowledge of monitoring and observability tools (Prometheus, Grafana, OpenObserve) and their application to ML systems
• Experience with GPU infrastructure management and distributed computing frameworks for machine learning
• Familiarity with MLOps practices and tools for model deployment, versioning, and lifecycle management
• Strong scripting skills in Python, Bash, or similar languages for automation tasks
• Understanding of networking, security, and database management in cloud environments
• Experience with high-performance computing environments and job scheduling systems is a plus
• Knowledge of machine learning workflows and the unique infrastructure requirements of ML training and inference
• Strong problem-solving skills and ability to work in a fast-paced, collaborative environment
• Excellent communication skills and experience working with cross-functional teams

Compensation

• Competitive salary + significant stock options
• 30 days of holiday, plus bank holidays, per year
• Flexible working from home and 6 month remote working policies
• Enhanced parental leave
• Learning budget of £500 per calendar year for books, training courses and conferences
• Company pension scheme
• Regular team socials and quarterly all-company parties
• Bike2Work scheme

Join the fast-growing AgileRL team and play a key role in the development of cutting-edge reinforcement learning tooling and infrastructure.

Note: for the following longer form questions we have received an overwhelming number of applications with answers that are AI generated. Any application that uses AI generated answers will not be considered.

• What motivates you to apply to this role, and what are you looking forward to in contributing towards the AgileRL mission? (200 words max)
• What unique experience do you have with building and managing infrastructure for machine learning or data intensive platforms that makes you the ideal candidate for this role? (200 words max)
• What three infrastructure or operational improvements you believe would be most valuable for scaling a reinforcement learning platform like Arena, and how would you implement them? (200 words max)

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