Software Engineer

What you'll do

• Lead the AI and ML roadmap for the team, identifying high-value opportunities and prioritising against business impact
• Translate strategic goals into a clear, sequenced plan of ML initiatives
• Design, build and maintain end-to-end ML pipelines covering data ingestion, feature engineering, training, validation, deployment and retraining
• Ensure reproducibility, scalability and observability in ML pipelines
• Own model evaluation by defining offline and online metrics, building evaluation sets, running A/B tests and validating models for accuracy, fairness, robustness and business impact
• Establish MLOps best practices including experiment tracking, model registry, versioning, CI/CD for models, and infrastructure-as-code
• Maintain clear technical documentation
• Monitor models in production, detect drift, debug performance regressions, and iterate to keep latency, cost and accuracy within agreed thresholds
• Partner with developers, data engineers and Product Managers to expose models via APIs
• Work with specialism leads to embed ML capabilities into Media team workflows
• Collaborate with internal Security and Legal teams to ensure compliance with Security Policies, data handling standards and responsible AI principles

Requirements

• Strong experience training, fine-tuning and deploying machine learning models in production
• Solid grounding in classical ML and modern deep learning
• Strong Python skills
• Hands-on experience with ML frameworks such as PyTorch, TensorFlow, scikit-learn and Hugging Face
• Experience working with SQL and large-scale data tooling
• Experience building ML pipelines and MLOps tooling (e.g. Airflow, Kubeflow, MLflow, Weights & Biases, Vertex AI or SageMaker)
• Experience deploying models on cloud platforms (preferably GCP)
• Well versed in agile methodologies, Git and version control best practices
• Deep experience with model evaluation including offline metrics, eval set design, A/B testing, drift detection, fairness checks and validating models against business KPIs
• Comfortable with Docker, containerised model serving and exposing models via APIs
• Good communication skills and ability to communicate complex ideas
• Ability to comprehend business challenges and articulate potential solutions
• Strong attention to detail and highly organised
• Ability to self-manage and work as part of a wider team
• Belief in clean coding and simple solutions
• Outcome-focused and able to prioritise ML roadmap against competing demands

Tech stack