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- Tasks: Own the full ML model lifecycle from training to deployment and monitoring.
- Company: Join Optima Partners, a leading data consultancy transforming major brands.
- Benefits: Enjoy competitive salary, generous holiday, and private medical insurance.
- Why this job: Make a real impact by building production-ready ML systems with cutting-edge tools.
- Qualifications: 3 years in ML Engineering or MLOps with strong MLflow experience.
- Other info: Fast-growing team with excellent career growth opportunities and supportive culture.
The predicted salary is between 60000 - 70000 £ per year.
About Optima Partners
We’re an advanced data & business consultancy helping major consumer brands transform through data-driven customer strategy, design, and engineering. Our clients include leading UK and global brands across financial services, retail, and more. Join a practitioner-led, fast-growing team delivering ML systems that actually make it into production.
We’re growing our engineering team and looking for an MLOps Engineer who loves building real, production grade ML systems not just proof‑of‑concepts. If you enjoy working across the full ML lifecycle and have strong MLflow experience, this role is built for you.
What You’ll Do
- Own end-to-end ML model lifecycles from training to deployment to monitoring
- Productionise models built in PyTorch, TensorFlow, or Scikit‑learn
- Use MLflow for experiment tracking, model versioning, deployment, and release workflows
- Build scalable data & ML pipelines in Databricks (Delta, DLT, Jobs)
- Design and maintain Azure data architectures (Data Lake, Synapse, SQL DW)
- Create robust CI/CD pipelines for ML data using Azure DevOps, Git, Jenkins
- Automate infrastructure with Terraform / ARM / Ansible
- Work closely with engineering teams to deliver real-world ML systems across multiple sectors.
Tech Stack You’ll Work With
- Python, SQL, PySpark
- PyTorch, TensorFlow, Scikit‑learn
- MLflow (core focus), Databricks, Azure
- IaC: Terraform, ARM, Ansible
- Git, Azure DevOps, Jenkins
What We’re Looking For
- 3 years in ML Engineering, MLOps, or Data Engineering
- Hands-on experience productionising ML models
- Strong MLflow and Databricks experience
- Consulting experience is a plus
- Comfortable working in Agile environments
Join us to build production‑ready ML systems that make a real impact working with modern tooling, great people, and the freedom, support, and rewards to grow your career fast.
What We Offer
- Competitive base salary
- 15% discretionary company bonus
- Up to 37 days’ holiday
- Private medical insurance
- Group life assurance & income protection
- Salary sacrifice pension scheme
Locations
Senior ML/MLOps Engineer in City of London, London employer: Optima Partners
Contact Detail:
Optima Partners Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior ML/MLOps Engineer in City of London, London
✨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 ML projects, especially those using MLflow and Databricks. 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 your technical knowledge and problem-solving skills. Practice common ML and MLOps questions, and be ready to discuss your past experiences in detail.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Plus, it’s a great way to ensure your application gets seen by the right people.
We think you need these skills to ace Senior ML/MLOps Engineer in City of London, London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Senior ML/MLOps Engineer role. Highlight your experience with MLflow, Databricks, and any relevant projects that showcase your skills in productionising ML models.
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 Optima Partners. Be genuine and let your personality come through.
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 your previous job, we want to see how you’ve applied your skills in real-world scenarios.
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 you’re keen on joining our team!
How to prepare for a job interview at Optima Partners
✨Know Your MLflow Inside Out
Since this role focuses heavily on MLflow, make sure you can discuss your experience with it in detail. Prepare examples of how you've used MLflow for experiment tracking, model versioning, and deployment workflows. Being able to articulate specific challenges you faced and how you overcame them will impress the interviewers.
✨Showcase Your End-to-End ML Lifecycle Experience
Be ready to talk about your experience managing the entire ML lifecycle. Highlight projects where you took models from training to deployment and monitoring. Discuss the tools you used, like PyTorch or TensorFlow, and how you ensured the models were production-ready. This will demonstrate your hands-on expertise.
✨Familiarise Yourself with the Tech Stack
Brush up on the technologies mentioned in the job description, especially Databricks and Azure. Be prepared to discuss how you've built scalable data and ML pipelines using these tools. If you have experience with CI/CD pipelines using Azure DevOps or Jenkins, make sure to highlight that as well.
✨Prepare for Agile Environment Questions
Since the company values Agile methodologies, think about your experiences working in Agile teams. Be ready to share how you’ve collaborated with cross-functional teams to deliver ML systems. This will show that you can adapt to their work culture and contribute effectively.