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- Tasks: Design and maintain cloud infrastructure for cutting-edge reinforcement learning platforms.
- Company: Join a fast-growing tech team focused on innovative AI solutions.
- Benefits: Competitive salary, stock options, 30 days holiday, and flexible working.
- Why this job: Be at the forefront of AI technology and make a real impact.
- Qualifications: Experience in DevOps, cloud platforms, and containerisation technologies required.
- Other info: Dynamic environment with excellent career growth and learning opportunities.
The predicted salary is between 36000 - 60000 £ per year.
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.
DevOps Engineer in London employer: AgileRL Ltd
Contact Detail:
AgileRL Ltd Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land DevOps Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. A personal connection can often get your foot in the door faster than any application.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to cloud infrastructure and CI/CD pipelines. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on common DevOps scenarios and challenges. Practise explaining your thought process and solutions clearly, as communication is key in collaborative environments.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace DevOps Engineer in London
Some tips for your application 🫡
Be Authentic: When answering the longer-form questions, let your personality shine through! We want to hear your genuine thoughts and experiences, so avoid using AI-generated responses. Show us what makes you tick and why you're excited about joining our team.
Tailor Your Responses: Make sure to align your answers with the job description. Highlight your relevant experience in cloud infrastructure, CI/CD pipelines, and containerisation. This will help us see how you fit into the role and contribute to our mission at AgileRL.
Showcase Your Skills: Don’t hold back on sharing your technical skills! Whether it’s your expertise in Docker, Kubernetes, or Infrastructure as Code tools, make sure to mention them. We’re looking for someone who can hit the ground running, so let us know what you bring to the table.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets to us without any hiccups. Plus, it shows you’re keen on being part of our community right from the start!
How to prepare for a job interview at AgileRL Ltd
✨Know Your Tech Stack
Make sure you’re well-versed in the specific technologies mentioned in the job description, like AWS, Docker, and Kubernetes. Brush up on your knowledge of CI/CD principles and tools, as well as Infrastructure as Code solutions. Being able to discuss these confidently will show that you're ready to hit the ground running.
✨Showcase Your Problem-Solving Skills
Prepare examples from your past experiences where you've tackled complex issues, especially in cloud infrastructure or ML workloads. Use the STAR method (Situation, Task, Action, Result) to structure your answers. This will help interviewers see how you approach challenges and find effective solutions.
✨Understand the Company’s Mission
Familiarise yourself with AgileRL's mission and how the role of a DevOps Engineer fits into it. Be ready to articulate why you’re excited about contributing to their reinforcement learning platform. This shows genuine interest and alignment with their goals, which can set you apart from other candidates.
✨Prepare for Technical Questions
Expect technical questions that assess your understanding of cloud infrastructure, containerisation, and monitoring tools. Practice explaining your thought process clearly and concisely. You might even want to do some mock interviews with friends or colleagues to get comfortable with articulating your technical knowledge.
