Machine Learning Engineer

Responsibilities

• Take models from prototype to production, turning data scientists' experimental work into robust, tested, performant systems that run reliably at scale across our Core Intelligence Services.
• Own feature engineering and ML-specific data quality: training-data validation, feature and label integrity, leakage and skew checks.
• Take ownership of deploying, serving and monitoring models in production—drift and performance monitoring, retraining triggers, and ensuring reliability of ML workloads.
• Work with the DevOps team and Lead Data Engineer to shape practical deployment patterns across the group.
• Shape evaluation approaches, retraining logic, and inference‑cost and performance improvements to define ML engineering standards across the Data function.
• Partner day‑to‑day with data scientists on modelling, and with infrastructure engineering to ensure models deploy cleanly on the platform.
• Set the practical standard for ML engineering, reproducibility, testing and model review, leading by example within the team.

Qualifications

• Hands‑on experience taking ML models into production.
• Strong software‑engineering fundamentals: production‑level Python, testing, version control and code review.
• Sound grasp of the full ML lifecycle: feature engineering, model development and evaluation, and awareness of failure modes (drift, skew, data quality) in production.
• Comfortable owning deployment and monitoring of own models—including CI/CD for ML and operational instinct to keep workloads healthy.
• Exposure to forecasting, optimisation or recommendation systems, or a clear aptitude to learn them quickly.
• Practical experience with modern data platforms (Snowflake, Databricks, AWS/Azure) and close collaboration with data engineering on model‑feeding data.
• Ability to operate independently in a lean environment—own delivery end‑to‑end and make sound technical calls with light direction.

Preferred Skills

• Previous experience within the Retail and Commerce Media space or other AdTech platforms.
• Familiarity with MLOps tooling such as MLflow, orchestration and model registries, and feature stores.
• Experience with LLM systems—RAG, agentic patterns, evals, or productionising foundation‑model workflows.
• Experience in a lean or one‑deep team where breadth and depth have been built.