- Professional
- Optionales Büro in London
Our Team
Data Science is part of the wider Strategy, Data and Insight team and plays a critical role at the heart of UKTV’s data science capabilities. By building and maintaining the infrastructure, tools, and workflows that power machine learning, we enable the delivery of reliable, scalable, and production-ready AI solutions. Our work ensures that data science outputs can be seamlessly integrated into business processes, supporting strategic and operational decision-making across the organization and driving innovation and growth across the U app, and our linear channel brands.
Purpose of the role
The MLOps Engineer will play a major role in shaping how we design, implement, and scale our machine learning operations. They will bring expertise that enables our data science models to move seamlessly from development to production, ensuring reliability, scalability, and performance. This role is essential in helping our colleagues leverage AI solutions effectively in decision-making, supporting a deeper understanding of our audiences, and enabling the adoption of new technologies through robust, automated, and well-governed ML workflows.
This is a greenfield, hands-on role where you will have the opportunity to define and embed best-in-class MLOps practices across UKTV, building the infrastructure and processes that will underpin our AI strategy for years to come. You will also be involved in sharing best practice and embedding a new data platform in collaboration with our parent company, BBC Studios.
What we would like you to bring to the role….key experience, knowledge, skills & personal qualities
Experience
- Developing and maintaining ML pipelines for training, testing, deployment, and monitoring of machine learning models.
- Transitioning models from development to production in collaboration with Lead Data Scientists.
- Providing technical support to analysts, including enabling tool access, data integration, and workflows.
- Building and maintaining tools for model tracking, versioning, and experiment management.
- Integrating CI/CD practices into the ML lifecycle for faster and more reliable deployments.
- Implementing monitoring solutions for model performance and data drift.
- Optimizing infrastructure and workflows for scalability, cost-efficiency, and performance.
- Supporting troubleshooting and incident response for ML services in production.
- Managing APIs and data connections, including integration with analytics tools (e.g., Tableau).
- Implementing and managing orchestration solutions for ML workflows.
- Utilizing AWS Lambda and other serverless technologies for scalable, event-driven processes.
- Collaborating across teams, applying strong communication, problem-solving, and stakeholder management skills.
- Adapting to changing priorities, managing multiple projects, and maintaining detail under pressure.
- Working in broadcast, production, start-up/scale-up, or media/marketing/advertising contexts.
- Operationalizing ML models in commercial, direct-to-consumer organizations.
- Collaborating with agencies and external technical partners on deployment or integration projects.
- Applying agile methodologies and product development practices, including continuous delivery of ML and analytics capabilities.
Knowledge
- Best practices for ML pipeline design, deployment, and maintenance.
- Model tracking, versioning, and experiment management tools and techniques.
- CI/CD principles and tools relevant to ML workflows.
- Monitoring approaches for ML model performance and detecting data drift.
- Infrastructure optimization strategies for scalability, cost control, and performance enhancement.
- API management and integration approaches for analytics platforms (e.g., Tableau).
- Orchestration tools and solutions for automating ML workflows.
- AWS Lambda and serverless architectures for ML applications.
- Communication, collaboration, and stakeholder engagement best practices in technical environments.
- AdTech and MarTech ecosystems, including Customer Data Platforms and Data Clean Rooms.
- Data infrastructure and deployment considerations in broadcast, streaming, and advertising industries.
Skills & personal qualities
- Bachelor’s degree in computer science, software engineering, data engineering, or related field; master’s degree preferred OR similar knowledge gained through relevant experience.
- Ability to design solutions to loosely defined operational and deployment challenges, ensuring ML models can be reliably delivered, scaled, and maintained in production.
- Proficiency in programming with Python, including experience with ML frameworks and libraries (e.g., scikit-learn, TensorFlow, PyTorch).
- Strong understanding of CI/CD practices, containerization (e.g., Docker), orchestration tools (e.g., Kubernetes, Airflow), and cloud platforms (e.g., AWS, SageMaker).
- Experience integrating and automating workflows across large, varied datasets, and architecting specialized database and computing environments for ML workloads.
- Skilled at simplifying complex technical problems into clear, actionable components and resolving them systematically.
- Able to effectively delegate, manage your own time, and priorities work across multiple concurrent projects and environments.
- Confident making operational decisions in unprecedented situations, and able to provide informed recommendations with authority and clarity.
- Capable of developing strong relationships with senior stakeholders, analysts, and data scientists, and adept at collaborating across departments.
- Communicates effectively using both technical and non-technical language, adapting to stakeholders at all levels.
- Strong attention to detail, critical thinking skills, and the ability to work under pressure in production environments.
- Demonstrates excellent problem-solving, incident management, and team motivation skills.
What you will get to work on….key outputs and responsibilities of the role
- Design, develop, and maintain production-ready ML pipelines to support targeted advertising, personalized recommendations, and other AI-driven features on the U app.
- Operationalize propensity models, audience segmentation models, and other predictive assets developed by the Data Science team, ensuring seamless deployment and ongoing performance monitoring.
- Implement robust monitoring, alerting, and retraining strategies to maintain model accuracy, detect data drift, and minimize downtime.
- Build and manage APIs, orchestration processes, and integrations with analytical tools (Tableau) to ensure analysts and stakeholders can easily access model outputs and insights.
- Optimize ML infrastructure for scalability, cost-efficiency, and performance using AWS services (including SageMaker, Lambda, and containerized deployments).
- Support ad hoc operational requests, including deploying proof-of-concept ML projects into production environments.
- Collaborate with Data Scientists and Analysts, to improve data quality, metadata management, and data pipelines that feed ML models.
- Stay up to date with industry trends and best practices in MLOps.
- On occasion, lead or contribute to pan-BBC MLOps projects, collaborating with the BBC Studios Data Science team and those maintaining the BBC Studios Data Platform to share learnings and ensure platform consistency.
- Provide technical guidance and mentoring to team members working on ML deployment and operational tasks.
