At a Glance
- Tasks: Operationalise machine learning, deploy and maintain models in secure environments.
- Company: Affinity Water, committed to innovation and inclusivity.
- Benefits: Competitive salary, flexible working, generous leave, and wellness support.
- Other info: Opportunities for learning, mentoring, and community volunteering.
- Why this job: Join a dynamic team and turn advanced analytics into real business value.
- Qualifications: 5+ years in MLOps or DevOps, strong Python and cloud experience.
The predicted salary is between 42000 - 84000 € per year.
We are looking for a Machine Learning Ops Engineer to help operationalise machine learning across Affinity Water on a 24 month FTC. You will work with data scientists, engineers, architects, and stakeholders to deploy, monitor, and maintain ML models in robust, scalable, and secure production environments, turning advanced analytics into sustained business value.
What You Will Do:
- Build, maintain, and optimise automated ML deployment pipelines with CI/CD, containerisation, and orchestration.
- Monitor model performance, data drift, and system health to ensure reliability and availability.
- Support ML platforms and infrastructure on-premise or in the cloud (AWS, SageMaker), ensuring scalability and security.
- Collaborate with data scientists to productionise models and embed ML Ops best practices across the organisation.
- Ensure governance, compliance, documentation, and reproducibility of ML pipelines and models.
- Provide 2nd/3rd line support, manage release cycles, and resolve incidents efficiently.
- Continuously improve ML Ops processes, tooling, and automation for efficiency and reliability.
What We Are Looking For:
Essential:
- 5+ years experience in MLOps, DevOps, or related roles.
- Strong knowledge of ML lifecycle management, deployment, monitoring, and model maintenance.
- Hands-on experience with Python (and ML frameworks), containerisation (Docker/Kubernetes), CI/CD pipelines, and cloud ML services (AWS SageMaker preferred).
- Experience with infrastructure-as-code, production-grade Linux environments, and API services (Flask/Gunicorn).
- Proficiency in building automated, reliable ML pipelines with structured and unstructured data.
- Excellent problem-solving, analytical, and communication skills; self-motivated and organised.
- Ability to embed best practices for governance, reproducibility, and operational excellence.
Desirable:
- Experience with feature stores, model registries, real-time serving, and model retraining automation.
- Integration of ML systems into business applications or APIs.
- Exposure to Water Industry data, systems, and processes.
Benefits:
- Salary: From £60,000 dependant on skills and experience.
- Able to work from the Hatfield office at least 2 days per week, with flexibility to spend additional days on-site as required by the programme.
- Learning and development opportunities, including mentoring and a range of formal courses and open learning resources.
- Entry into the company annual bonus scheme.
- Annual leave from 26-30 rising with length of service, and the option to purchase up to 5 extra days.
- A Celebration Day in addition to public holidays that people can use to celebrate a religious festival or other occasion that is important to them.
- A generous 'double match pension scheme' that doubles the contributions you make (company contribution capped at 12%).
- We offer a range of family benefits including enhanced Maternity, Adoption, Paternity, Shared Parental Leave, Fertility Support Leave and up to 5 full or 10 half days of paid Carers Leave.
- Menopause policy and Reasonable Adjustment policy to help everyone perform at their best.
- Access to our Wellbeing Centre with support for looking after your physical and mental health.
- Discounts at a Range of Retail Outlets and on Dental and Medical Insurance through our Tap4Perks scheme.
- Up to 4 Affinity days a year to volunteer in the community.
- Life Assurance.
As a Disability Confident employer, we are committed to offering interviews to disabled candidates who meet the essential criteria and opt in on the application form. Affinity Water recognises the benefits of greater diversity in our workforce to better reflect the communities we serve. We are committed to building a more inclusive culture where every member of our workforce can thrive.
Machine Learning Ops Engineer in Watford employer: Affinity Water Limited
Affinity Water is an exceptional employer that prioritises employee growth and well-being, offering a supportive work culture where collaboration with data scientists and engineers leads to impactful projects in the water industry. With flexible working arrangements from the Hatfield office, generous benefits including a double match pension scheme, and a commitment to diversity and inclusion, employees can thrive both personally and professionally while contributing to meaningful advancements in machine learning operations.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Ops Engineer in Watford
✨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, deployment pipelines, and any cool stuff you've built. 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 are proactive and engaged with our company.
We think you need these skills to ace Machine Learning Ops Engineer in Watford
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Machine Learning Ops Engineer role. Highlight your experience with MLOps, CI/CD, and cloud services like AWS. We want to see how your skills match what we're looking for!
Showcase Your Projects:Include specific projects where you've built or maintained ML pipelines. We love seeing real-world examples of your work, especially if they demonstrate your problem-solving skills and ability to collaborate with teams.
Be Clear and Concise:When writing your application, keep it clear and to the point. Use bullet points for key achievements and avoid jargon unless it's relevant. We appreciate straightforward communication that gets to the heart of your experience.
Apply Through Our Website:Don't forget to apply 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 at Affinity Water!
How to prepare for a job interview at Affinity Water Limited
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, especially Python, Docker, Kubernetes, and AWS SageMaker. Brush up on your CI/CD pipeline knowledge and be ready to discuss how you've used these tools in past projects.
✨Showcase Your Problem-Solving Skills
Prepare examples of how you've tackled challenges in MLOps or DevOps roles. Think about specific incidents where you resolved issues with model performance or deployment. This will demonstrate your analytical skills and ability to think on your feet.
✨Understand the Business Impact
Be ready to discuss how your work in ML Ops can translate into business value. Familiarise yourself with how machine learning can optimise processes in the water industry, and be prepared to share ideas on how to operationalise ML effectively.
✨Ask Insightful Questions
Prepare thoughtful questions about the team dynamics, current ML projects, and the company’s approach to governance and compliance. This shows your genuine interest in the role and helps you gauge if the company culture aligns with your values.