Machine Learning Operations Engineer
Retail Insight
- London, England, United Kingdom
- London, England, United Kingdom
About
Machine Learning Operations Engineer
role at
Retail Insight
At Retail 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 an 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
Benefits
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 set‑up 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!
Equal Opportunity Statement 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.
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Languages
- English
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