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- Tasks: Manage and optimise ML/AI artefacts using automated frameworks for production-ready software.
- Company: Join Drax Group, a leader in tackling climate challenges through innovative technology.
- Benefits: Enjoy competitive salary, 25 days leave, private medical insurance, and a pension scheme.
- Why this job: Be part of impactful projects while shaping your career in a supportive, inclusive environment.
- Qualifications: Strong experience in ML Ops, Python, SQL, and various ML frameworks required.
- Other info: Opportunity to work across multiple projects and contribute to a sustainable future.
The predicted salary is between 36000 - 60000 £ per year.
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Machine Learning Operations (ML Ops) Engineer, Selby
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Client:
Drax Group
Location:
Selby, United Kingdom
Job Category:
Other
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EU work permit required:
Yes
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Job Reference:
a763f4d5d9b2
Job Views:
3
Posted:
29.06.2025
Expiry Date:
13.08.2025
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Job Description:
About the Role:
As a Machine Learning Operations (MLOps) Engineer, you’ll be responsible for managing, releasing and monitoring Machine Learning (ML) and Artificial Intelligence (AI) artefacts using automated frameworks. You’ll also optimise ML/AI code written by our Data Scientists into Production-ready software according to agreed performance and cost criteria.
You’ll play a key role ensuring that ML/AI projects are setup for success via the automation of residual manual steps in the development and production lifecycle. You’ll also provide essential insights into the ongoing predictive capability and cost of deployed ML/AI assets using language and visualisations appropriate for your audience.
It’s an opportunity to work across multiple projects concurrently. You’ll use your judgement to determine which projects and teams need most of your time. You’ll contribute to early engagements through strong communication skills, domain experience and knowledge gathered throughout your career.
This role requires you to have adept time-management and prioritisation skills to keep on top of your responsibilities. You’ll use your cross-project exposure to feedback to the Data & Data Science Leadership Team to guide understanding, improve consistency, and develop & implement initiatives to improve the community for the future.
Who we’re looking for
You’ll need strong experience delivering and monitoring and scalable ML/AI solutions via automated ML Ops.
Ideally, you’ll also be technically skilled in most or all of the below:
– Expert knowledge of Python and SQL, inc. the following libraries: Numpy, Pandas, PySpark and Spark SQL
– Expert knowledge of ML Ops frameworks in the following categories:
a) experiment tracking and model metadata management ( MLflow)
b) orchestration of ML workflows ( Metaflow)
c) data and pipeline versioning ( Data Version Control)
d) model deployment, serving and monitoring ( Kubeflow)
– Expert knowledge of automated artefact deployment using YAML based CI/CD pipelines and Terraform
– Working knowledge of one or more ML engineering frameworks ( TensorFlow, PyTorch, Keras, Scikit-Learn)
– Working knowledge of object-oriented programming and unit testing in Python
– Working knowledge of application and information security principles and practices ( OWASP for Machine Learning)
– Working knowledge of Unix-based CLI commands, source control and scripting
– Working knowledge of containerisation ( Docker) and container orchestration ( Kubernetes)
– Working knowledge of a cloud data platform ( Databricks) and a data lakehouse architecture ( Delta Lake)
– Working knowledge of the AWS cloud technology stack ( S3, Glue, DynamoDB, IAM, Lambdas, ELB, EKS)
Rewards and benefits
As you help us to shape the future, we’ve shaped our rewards and benefits to help you thrive and support your lifestyle:
– Competitive salary
– Discretionary group performance-based bonus
– 25 days annual leave (plus Bank Holidays)
– Single cover private medical insurance
– Pension scheme
We’re committed to making a tangible impact on the climate challenge we all face. Drax is where your individual purpose can work alongside your career drive. We work as part of a team that shares a passion for doing what’s right for the future. With Drax you can shape your career and a future for generations to come.
Together, we make it happen.
At Drax, we’re committed to fostering an environment where everyone feels valued and respected, regardless of their role. To make this a reality, we actively work to better represent the communities we operate in, foster inclusion, and establish fair processes. Through these actions, we build the trust needed for all colleagues at Drax to contribute their perspectives and talents, no matter their background. Find out more about our approachhere.
How to apply
Think this role’s foryou? Click the ‘apply now’ button to begin your Drax journey.
If you want to find outmore about Drax, check out our LinkedIn page to see our latestnews.
We understand that youmay have some additional questions about the role. If you’d like to have aconfidential chat to discuss the role in more detail, please email
Wereserve the right to close roles early when the particular role and / orlocation has had sufficient applications.
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Machine Learning Operations (ML Ops) Engineer employer: Drax Group
Contact Detail:
Drax Group Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Operations (ML Ops) Engineer
✨Tip Number 1
Familiarise yourself with the specific ML Ops frameworks mentioned in the job description, such as MLflow and Kubeflow. Having hands-on experience or projects showcasing your skills with these tools can set you apart from other candidates.
✨Tip Number 2
Network with professionals in the ML Ops field, especially those who work at Drax Group or similar companies. Engaging in conversations on platforms like LinkedIn can provide insights into the company culture and expectations, which can be beneficial during interviews.
✨Tip Number 3
Stay updated on the latest trends and advancements in machine learning and AI. Being able to discuss recent developments or innovations during your interview can demonstrate your passion and commitment to the field.
✨Tip Number 4
Prepare to showcase your problem-solving skills through practical examples. Be ready to discuss how you've optimised ML/AI code or automated processes in previous roles, as this aligns closely with the responsibilities of the position.
We think you need these skills to ace Machine Learning Operations (ML Ops) Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Machine Learning Operations and the specific technologies mentioned in the job description, such as Python, SQL, and ML Ops frameworks. Use keywords from the job listing to ensure your application stands out.
Craft a Compelling Cover Letter: Write a cover letter that not only outlines your technical skills but also demonstrates your understanding of Drax Group's mission and values. Explain how your background aligns with their goals, particularly in optimising ML/AI projects.
Showcase Relevant Projects: Include examples of past projects where you successfully implemented ML Ops solutions. Detail your role, the technologies used, and the impact of your work. This will provide concrete evidence of your capabilities.
Proofread and Edit: Before submitting your application, carefully proofread your documents for any spelling or grammatical errors. A polished application reflects your attention to detail, which is crucial for a role that involves managing and monitoring ML/AI artefacts.
How to prepare for a job interview at Drax Group
✨Showcase Your Technical Skills
Be prepared to discuss your expertise in Python, SQL, and the various ML Ops frameworks mentioned in the job description. Bring examples of past projects where you've successfully implemented these technologies.
✨Demonstrate Problem-Solving Abilities
Expect to face scenario-based questions that assess your ability to troubleshoot and optimise ML/AI solutions. Think of specific challenges you've encountered and how you overcame them.
✨Communicate Clearly
Strong communication skills are essential for this role. Practice explaining complex technical concepts in simple terms, as you'll need to convey insights to diverse audiences.
✨Prepare for Time Management Questions
Given the need for adept time-management and prioritisation skills, be ready to discuss how you manage multiple projects simultaneously. Share strategies you've used to stay organised and focused.
