At a Glance
- Tasks: Build and optimise scalable MLOps and DevOps solutions using AWS SageMaker.
- Company: Join a fast-paced tech company driving enterprise-scale transformation.
- Benefits: Remote-first work, flexible options, and exposure to cutting-edge AI projects.
- Other info: Collaborative culture with excellent career growth opportunities.
- Why this job: Make a real impact on innovative AI and cloud initiatives.
- Qualifications: 6+ years in DevOps or Cloud Engineering with expert AWS knowledge.
The predicted salary is between 60000 - 80000 € per year.
Join an innovative and fast‑paced technology environment where cloud infrastructure, automation, and machine learning come together to drive enterprise‑scale transformation. In this role, you will play a key part in building and optimizing scalable MLOps and DevOps solutions focused on AWS SageMaker environments. You’ll collaborate with cross‑functional engineering teams to automate machine learning workflows, streamline deployments, and improve operational efficiency across complex AI ecosystems. This opportunity is ideal for a technically strong engineer who enjoys solving infrastructure challenges and enabling data science teams to move faster and more reliably. Working remotely within an international environment, you’ll contribute to cutting‑edge AI and cloud initiatives while leveraging modern DevOps best practices. The role offers strong ownership, exposure to advanced cloud technologies, and the ability to make a direct impact on enterprise AI delivery.
Accountabilities
- Design, implement, and automate enterprise‑grade AWS SageMaker environments and infrastructure solutions.
- Develop DevOps automation frameworks to support scalable machine learning operations and infrastructure management.
- Configure and maintain SageMaker lifecycle policies, ensuring optimized and reliable environments for ML teams.
- Build and manage CI/CD pipelines that enable users to deploy custom Docker images, kernels, and ML workloads efficiently.
- Create monitoring, alerting, and cost‑control mechanisms for SageMaker services and enterprise ML projects.
- Support MLOps initiatives by automating model deployment processes and infrastructure promotion across environments.
- Collaborate with engineering, DevOps, and data teams to improve operational workflows and system reliability.
- Continuously enhance infrastructure scalability, performance, and security following DevOps and cloud best practices.
Requirements
- 6+ years of experience in DevOps, Cloud Engineering, MLOps, or related technical roles.
- Expert‑level knowledge of AWS services and strong proficiency in Python programming.
- Hands‑on experience working with AWS SageMaker and machine learning infrastructure environments.
- Proven experience building enterprise‑scale DevOps automation and cloud deployment solutions.
- Strong understanding of CI/CD concepts, infrastructure automation, and cloud‑native architectures.
- Experience creating deployment pipelines using tools such as Jenkins is highly valued.
- Familiarity with Docker, containerized environments, and ML workflow automation is preferred.
- Experience with MLOps practices, model deployment automation, and monitoring solutions is a plus.
- Strong analytical and problem‑solving abilities with a proactive and detail‑oriented mindset.
- Excellent communication and collaboration skills in remote and cross‑functional environments.
Benefits
- Remote‑first work environment across Europe.
- Opportunity to work on cutting‑edge AI, cloud, and MLOps projects.
- Flexible employment options (Contractor or Full‑time).
- Exposure to enterprise‑scale cloud infrastructure initiatives.
- Collaborative and innovation‑driven engineering culture.
- Career growth opportunities in advanced cloud and AI technologies.
- Work alongside highly skilled international technical teams.
- Inclusive and diverse workplace committed to equal opportunities.
- Challenging and impactful projects with modern technology stacks.
Sagemaker DevOps Engineer in London employer: Jobgether
Join a forward-thinking company that champions innovation and collaboration in a remote-first environment across Europe. As a Sagemaker DevOps Engineer, you will engage with cutting-edge AI and cloud projects while enjoying flexible employment options and ample opportunities for career growth in advanced technologies. Our inclusive culture fosters teamwork and creativity, ensuring that you can make a meaningful impact on enterprise-scale transformations.
StudySmarter Expert Advice🤫
We think this is how you could land Sagemaker DevOps Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those working with AWS SageMaker or in MLOps. Join relevant online communities and attend meetups to make connections that could lead to job opportunities.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects related to DevOps and machine learning. This could be anything from GitHub repositories to personal blogs explaining your work with AWS services.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice common DevOps scenarios and be ready to discuss how you've tackled infrastructure challenges in the past.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals who can contribute to our innovative projects. Your next big opportunity could be just a click away!
We think you need these skills to ace Sagemaker DevOps Engineer in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with AWS SageMaker and DevOps practices. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re excited about this role at StudySmarter and how your background makes you a perfect fit for our innovative team.
Showcase Your Technical Skills:We love seeing hands-on experience! Be sure to mention any specific tools or technologies you've worked with, like CI/CD pipelines or Docker, as these are key to the role and will catch our eye.
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for this exciting opportunity in our remote-first environment.
How to prepare for a job interview at Jobgether
✨Know Your AWS Inside Out
Make sure you brush up on your AWS knowledge, especially around SageMaker. Be ready to discuss how you've used AWS services in past projects and how they relate to MLOps and DevOps. This will show that you’re not just familiar with the tools but can also leverage them effectively.
✨Showcase Your Automation Skills
Prepare examples of how you've developed automation frameworks or CI/CD pipelines in previous roles. Highlight specific tools like Jenkins or Docker that you've used, and be ready to explain the impact of your work on operational efficiency and deployment speed.
✨Collaboration is Key
Since this role involves working with cross-functional teams, think of instances where you've successfully collaborated with engineers, data scientists, or other stakeholders. Share how you improved workflows or resolved conflicts, as this will demonstrate your communication skills and teamwork.
✨Problem-Solving Mindset
Be prepared to discuss challenges you've faced in infrastructure management or machine learning deployments. Use the STAR method (Situation, Task, Action, Result) to structure your answers, showcasing your analytical skills and proactive approach to problem-solving.
