Take models from prototype to production, turning data scientists' experimental work into robust, tested, performant systems that run reliably at scale across SMG's Core Intelligence Services.
Feature engineering & ML data quality 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 your models in production
drift and performance monitoring, retraining triggers, and the reliability of ML workloads.
Working with the DevOps team and Lead Data Engineer, and helping shape the practical patterns for how this is done across the group.
Shape evaluation approaches, retraining logic, and inference-cost and performance improvements, helping define, not just follow, the ML engineering standards across the Data function.
Partner day-to-day with data scientists on modelling, and with infrastructure engineering to ensure models are built to deploy cleanly on the platform.
Set the practical standard for how we do ML engineering, reproducibility, testing and model review, leading by example within the team.

Hands-on experience taking ML models into production.
Strong software engineering fundamentals: production level Python, testing, version control and code review.
You write high-quality, secure, maintainable code others can build on.
Solid grasp of the full ML lifecycle: feature engineering, model development and evaluation, and the failure modes of models in production (drift, skew, data quality).
Comfortable owning deployment and monitoring of your own models
CI/CD for ML, and the operational instinct to keep production workloads healthy.
Exposure to at least one of forecasting, optimisation or recommendation systems, or clear aptitude to pick these up quickly.
Practical experience with modern data platforms (Snowflake, Databricks, AWS/Azure) and collaborating closely with data engineering on the data that feeds models.
Able to operate independently in a lean environment
owning delivery end to end and making sound technical calls with light direction.
Previous experience within the Retail and Commerce Media space, or with other AdTech platforms (desirable).
Familiarity with MLOps tooling (MLflow, orchestration, model registries) and feature stores (desirable).
Familiarity with LLM systems
RAG, agentic patterns, evals, or productionising foundation-model workflows (desirable).

Machine Learning Engineer

Key skills