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- Tasks: Build and maintain scalable ML pipelines, ensuring robust model deployment and performance.
- Company: Retail Insight transforms data into actionable strategies for retailers and CPGs.
- Benefits: Flexible working, 25 days leave, private medical insurance, and career development opportunities.
- Why this job: Join a dynamic team to innovate and make a real impact in the retail industry.
- Qualifications: Proven Python skills, experience with cloud platforms, and knowledge of MLOps best practices.
- Other info: Enjoy a supportive environment that values diversity and encourages personal growth.
The predicted salary is between 36000 - 60000 £ per year.
Turn Data into Action with Retail Insight.
AtRetail Insight (RI), we transform data into actionable strategies, empowering retailers and CPGs to make smarter decisions. Our cutting-edge algorithms and innovative retail execution products are trusted by many of the world’s leading companies to improve sales, profitability, and operational efficiency.
From tackling out-of-stocks and poor in-store execution to reducing waste, markdowns, and shrink, RI helps businesses unlock performance drivers through advanced analytics. Find out more about our journey here .
About the role:
We’re looking for a MLOps Engineer to help us operationalize machine learning at scale. This is a critical role at the intersection of data science and IT operations, ensuring our ML models are robust, reliable, and production-ready. You’ll build the infrastructure, automation, and pipelines that enable seamless deployment and ongoing performance of ML systems — accelerating innovation and helping us deliver value faster to our clients.
What you will do:
- Build Pipelines: Design and maintain scalable ML pipelines that automate the end-to-end lifecycle.
- Deploy & Monitor Models: Oversee deployment into production, monitoring performance and retraining as needed.
- Automation & CI/CD: Implement CI/CD pipelines for ML workflows, driving speed and reliability.
- Manage Infrastructure: Develop and maintain infrastructure for data, models, and computation using cloud and containerization technologies.
- Collaborate Across Teams: Partner with Data Science, Engineering, Operations, and Product to deliver seamless ML solutions.
- Establish Best Practices: Promote MLOps standards to ensure quality, scalability, and consistency.
- Innovate & Improve: Continuously evaluate new tools and techniques to evolve our MLOps capabilities
- Proven programming skills in Python, with experience in ML frameworks.
- Experience with cloud platforms (Snowflake, Azure, GCP, AWS).
- Skilled in containerization (Docker) and orchestration (Kubernetes).
- Knowledge of data engineering concepts (ETL, data warehousing, data lakes, databases).
- Experience with CI/CD automation for ML workflows.
- Familiarity with monitoring and logging tools for production ML models.
- Ability to work in agile, cross-functional teams.
- Relevant degree in Computer Science, Data Science, Engineering, or related field (preferred).
Nice to haves…
- Experience in a retail background would be beneficial
- Keen on continuous technical development, data analytics trends and tools
Some of our extras…
Flexible Working– Enjoy a hybrid work model (typically 2 days in the office) with flexibility based on business needs, plus a work from anywhere policy to give you freedom to explore.
Time Off– 25 days annual leave (+ bank holidays), increasing with length of service, plus an extra day off for your birthday! We also operate summer hours so you can make the most of the sunshine.
Learning & Development– Access a vast range of courses through our learning platform and benefit from structured career progression plans to support your growth.
Health & Wellbeing– Private Medical Insurance, a healthcare cash plan, and mental health support via Help@Hand. Plus, we’ll ensure you have a safe and productive home setup with a workspace assessment.
Giving Back– Take paid volunteer days to support your local community, donate to your chosen charity through salary sacrifice (we’ll match it!), and make a difference with Give as You Earn.
Extra Perks– A car purchase scheme to make buying a new car easier, plus access to additional benefits through our online platform, including gym discounts.
Plus much more!
Be your authentic self – Retail Insight is committed to promoting equal opportunities in employment. All employees and any job applicants will receive equal treatment. We actively seek to create an environment where everyone feels respected, supported, and encouraged to contribute their best work.
About Us
We’re a unique blend of retail expertise built up from extensive industry experience; mathematical talent that builds and maintains sophisticated algorithms; and engineering skill that handles vast volumes of data regularly.
W e combine leading-edge technology with cutting-edge thinking, to help you tackle today’s mission-critical operational challenges and maximize your retail potential.
We focus on the factors that drive sales and profit performance, minimise waste and loss, and increase operational efficiency. The result: dynamic solutions that provide actionable insights and unparalleled time to value.
Our values underpin everything we do at Retail Insight. They drive us, define us and shape our decision-making. They’re also the reason the world’s largest retailers and CPGs find working with us so easy.
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Machine Learning Operations Engineer employer: Retail Insight Limited
Contact Detail:
Retail Insight Limited Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Operations 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 put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to MLOps. 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 thought process and how you tackle challenges. Confidence is key, so get comfortable talking about your experience!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in joining our team at Retail Insight.
We think you need these skills to ace Machine Learning Operations Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the MLOps Engineer role. Highlight your experience with ML frameworks, cloud platforms, and CI/CD automation. We want to see how your skills align with what we do at Retail Insight!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Share your passion for machine learning and how you can contribute to our mission. Let us know why you're excited about working with us and how you can help drive innovation.
Showcase Your Projects: If you've worked on relevant projects, don’t hold back! Include links to your GitHub or any other portfolio showcasing your work with ML pipelines, containerization, or data engineering. We love seeing practical examples of your skills!
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 at Retail Insight!
How to prepare for a job interview at Retail Insight Limited
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, like Python, Docker, and Kubernetes. Brush up on your cloud platform knowledge too, whether it’s AWS, Azure, or GCP. Being able to discuss your experience with these tools will show that you’re ready to hit the ground running.
✨Showcase Your Problem-Solving Skills
Prepare examples of how you've tackled challenges in previous roles, especially those related to ML pipelines and model deployment. Use the STAR method (Situation, Task, Action, Result) to structure your answers, making it easy for the interviewer to see your thought process and impact.
✨Understand the Business Context
Familiarise yourself with Retail Insight's mission and the retail industry as a whole. Think about how MLOps can drive value for retailers and CPGs. This will not only help you answer questions more effectively but also demonstrate your genuine interest in the role and the company.
✨Ask Insightful Questions
Prepare thoughtful questions to ask at the end of your interview. Inquire about the team dynamics, ongoing projects, or how they measure success in MLOps. This shows that you’re engaged and eager to contribute to their goals, plus it gives you valuable insights into the company culture.
