DevOps Engineer London, UKAgileRL Ltd • City of London, England, United Kingdom
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DevOps Engineer London, UK
AgileRL Ltd
- City of London, England, United Kingdom
- City of London, England, United Kingdom
À propos
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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Compétences linguistiques
- English
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