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- Tasks: Own and scale ML infrastructure, transforming prototypes into reliable production systems.
- Company: Fast-growing AI and robotics organisation with a focus on innovation.
- Benefits: Competitive salary, equity participation, paid vacation, and office perks.
- Why this job: Make a real impact in AI while collaborating with talented teams.
- Qualifications: Experience in MLOps, strong Python skills, and cloud platform knowledge.
- Other info: Opportunities for international collaboration and career growth.
The predicted salary is between 43200 - 72000 £ per year.
This is a high-impact role within a fast-growing AI and robotics organisation focused on building advanced, scalable intelligent systems for real-world industrial applications. The position owns the machine learning infrastructure and MLOps foundations as products, platforms, and teams scale. You will play a key role in transforming machine learning prototypes into reliable production systems, defining pragmatic engineering standards, and enabling fast, safe delivery of ML-powered capabilities. The role combines hands-on engineering, architectural ownership, and close collaboration with engineering and product teams.
Responsibilities
- Own and scale the organisation's ML infrastructure and MLOps foundations
- Design pragmatic, production-ready system architectures that balance speed, reliability, and cost
- Build and maintain CI/CD pipelines for ML workflows and application delivery
- Productionise ML models including training, evaluation, deployment, monitoring, and rollback strategies
- Ensure reliability, observability, security, and performance across ML systems
- Automate infrastructure provisioning, deployments, and environment management using cloud-native tooling
- Partner closely with ML engineers, software engineers, and product teams to deliver ML features end-to-end
- Act as a technical leader through design reviews, mentorship, and by establishing engineering best practices
Required Experience & Skills
- Staff or lead-level experience in MLOps, DevOps, or Infrastructure Engineering, ideally within high-growth or startup environments
- Strong Python skills with hands-on experience using modern ML frameworks (e.g., PyTorch, TensorFlow, or similar)
- Experience working with major cloud platforms (AWS, GCP, or Azure)
- Proven production experience with Docker and Kubernetes
- Strong understanding of CI/CD systems (e.g., GitHub Actions, GitLab CI, ArgoCD)
- Experience with Infrastructure as Code tools such as Terraform and Helm
- Solid understanding of data engineering fundamentals and ML life cycle management
- Ability to design scalable systems without unnecessary complexity
- Strong debugging and problem-solving skills in distributed systems
- Ownership mindset with excellent communication and cross-functional collaboration skills
What’s Offered
- Competitive salary and equity participation
- Paid vacation in line with local labour regulations
- Opportunities for international collaboration and travel
- Office benefits including meals, snacks, and team events
If you are interested - please apply directly!
Lead Mlops Engineer employer: Randstad Technologies
Contact Detail:
Randstad Technologies Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Mlops Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. 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 MLOps projects, GitHub repositories, or any relevant work. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common MLOps questions and scenarios. Practice explaining your past experiences and how they relate to the role. Confidence is key, so get comfortable talking about your expertise!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who take that extra step to engage with us directly.
We think you need these skills to ace Lead Mlops Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Lead MLOps Engineer role. Highlight your experience with MLOps, DevOps, and any relevant projects that showcase your skills in Python and cloud platforms. We want to see how you can bring value to our team!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about MLOps and how your background aligns with our mission at StudySmarter. Keep it concise but impactful – we love a good story!
Showcase Your Projects: If you've worked on any cool ML projects or have experience with CI/CD pipelines, make sure to mention them! We’re keen to see how you’ve tackled challenges and what innovative solutions you've implemented in your previous roles.
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 updates. Plus, it shows you’re serious about joining our awesome team!
How to prepare for a job interview at Randstad Technologies
✨Know Your MLOps Inside Out
Make sure you’re well-versed in MLOps principles and practices. Brush up on your knowledge of CI/CD pipelines, cloud platforms, and the specific ML frameworks mentioned in the job description. Being able to discuss your hands-on experience with tools like Docker, Kubernetes, and Terraform will show that you’re ready to hit the ground running.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in previous roles, especially those related to scaling ML systems or debugging distributed systems. Use the STAR method (Situation, Task, Action, Result) to structure your answers, making it easy for the interviewer to see how you approach complex problems.
✨Demonstrate Collaboration and Leadership
Since this role involves working closely with various teams, be ready to share examples of how you’ve successfully collaborated with ML engineers, software engineers, and product teams. Highlight any leadership experiences, such as mentoring others or leading design reviews, to show you can take charge when needed.
✨Ask Insightful Questions
Prepare thoughtful questions about the company’s current ML infrastructure and future projects. This not only shows your genuine interest in the role but also gives you a chance to assess if the company aligns with your career goals. Questions about their engineering best practices or how they handle productionising ML models can spark engaging discussions.
