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For Companies At a Glance
- Tasks: Own and scale ML infrastructure, design production-ready architectures, and automate workflows.
- Company: Fast-growing AI and robotics organisation with a focus on real-world applications.
- Benefits: Competitive salary, equity participation, paid vacation, and office perks.
- Why this job: Transform machine learning prototypes into reliable systems and make a real impact.
- Qualifications: Experience in MLOps or DevOps, strong Python skills, and cloud platform knowledge.
- Other info: Opportunities for international collaboration and travel in a dynamic environment.
The predicted salary is between 48000 - 84000 £ per year.
If you like the sound of this role and want to relocate, the Client is willing to help facilitate this move! 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
Qualifications
- 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 lifecycle 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
Benefits
- 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 Recruitment
Contact Detail:
Randstad Technologies Recruitment 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 potential colleagues on LinkedIn. We all know that sometimes it’s not just what you know, but who you know that can land you that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your MLOps projects. We want to see your hands-on experience with Python, Docker, and cloud platforms. This is your chance to shine and demonstrate your expertise!
✨Tip Number 3
Prepare for those interviews! Brush up on your technical knowledge and be ready to discuss your past experiences in detail. We recommend practising common MLOps interview questions and even doing mock interviews with friends to build confidence.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we’re always looking for passionate individuals who are ready to make an impact in the AI and robotics space.
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, cloud platforms, and any relevant projects that showcase your skills in Python and CI/CD systems. 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 interesting ML projects, make sure to mention them! Whether it's a personal project or something from a previous job, we want to see your hands-on experience and problem-solving skills in action.
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’re considered for the role. Plus, it shows us you’re keen to join the StudySmarter family!
How to prepare for a job interview at Randstad Technologies Recruitment
✨Know Your MLOps Inside Out
Make sure you brush up on your MLOps knowledge, especially around CI/CD pipelines and cloud platforms like AWS or GCP. Be ready to discuss how you've implemented these in past projects, as this will show your hands-on experience and understanding of the role.
✨Showcase Your Python Skills
Since strong Python skills are a must for this position, prepare to demonstrate your coding abilities. You might be asked to solve a problem on the spot, so practice coding challenges related to ML frameworks like PyTorch or TensorFlow to feel confident.
✨Prepare for System Design Questions
Expect questions about designing scalable systems and balancing speed, reliability, and cost. Think through examples from your previous work where you’ve had to make these decisions, and be ready to explain your thought process clearly.
✨Emphasise Collaboration and Leadership
This role involves working closely with various teams, so highlight your experience in cross-functional collaboration. Share examples of how you've acted as a technical leader or mentor, as this will demonstrate your ability to guide others and establish best practices.
