At a Glance
- Tasks: Design and automate AWS SageMaker environments for scalable MLOps and DevOps solutions.
- Company: Join a fast-paced tech company driving enterprise-scale AI transformation.
- Benefits: Remote-first work, flexible options, and exposure to cutting-edge AI projects.
- Other info: Collaborative culture with excellent career growth opportunities in advanced technologies.
- Why this job: Make a real impact on innovative AI and cloud initiatives while enhancing your skills.
- Qualifications: 6+ years in DevOps or Cloud Engineering with strong AWS and Python expertise.
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 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'll have the opportunity to work on cutting-edge AI and cloud projects while enjoying flexible employment options and a culture that prioritises career growth and inclusivity. With access to advanced technologies and a diverse team of skilled professionals, you'll make a meaningful impact on enterprise-scale transformations in the world of machine learning.
StudySmarter Expert Advice🤫
We think this is how you could land Sagemaker DevOps Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, join relevant online communities, and attend meetups. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving AWS SageMaker and DevOps automation. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common technical questions related to MLOps and cloud infrastructure. Practice explaining your past projects and how you tackled challenges, as this will demonstrate your problem-solving abilities.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got some fantastic opportunities waiting for you, and applying directly can sometimes give you an edge. Plus, it’s super easy to keep track of your applications!
We think you need these skills to ace Sagemaker DevOps Engineer
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 and how you can contribute to our innovative environment. Keep it concise but impactful – we love a good story!
Showcase Your Technical Skills:Since this role is all about cloud infrastructure and automation, make sure to highlight your technical expertise in Python, CI/CD, and MLOps. We’re looking for someone who can hit the ground running, so let us know what you’ve got!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates. Plus, we love seeing applications come in through our own channels!
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. 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 implemented automation frameworks in previous roles. Discuss specific tools like Jenkins or Docker that you've used to streamline processes. This will demonstrate your hands-on experience and problem-solving abilities in a DevOps context.
✨Collaboration is Key
Since this role involves working with cross-functional teams, be ready to share experiences where you've collaborated with engineers, data scientists, or other stakeholders. Highlight how you improved workflows or system reliability through teamwork.
✨Ask Insightful Questions
Prepare thoughtful questions about the company's current MLOps initiatives or challenges they face with their cloud infrastructure. This shows your genuine interest in the role and helps you gauge if the company culture aligns with your values.
