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This is primarily a Data Engineering/MLOps role focusing on building data pipelines, infrastructure automation, and productionizing ML models. The title 'Python/ML Engineer' overemphasizes ML development; actual daily work is heavily oriented toward data engineering and DevOps.
This role is about building and maintaining data pipelines and ML infrastructure for a loss prevention project at a major UK retailer. You'll work with PySpark on Spark/Kubernetes clusters, orchestrate workflows with Airflow, manage Azure infrastructure with Terraform, and implement CI/CD with GitHub Actions. Although titled 'Python/ML Engineer', the core work is data engineering and DevOps for ML, not algorithm development. The team is small (3 engineers), so you'll have high ownership and impact on the technical roadmap.