Lead ML Engineer

• At least eight years of professional experience in Machine Learning, Data Science, or a related field.
• Advanced Python and SQL skills, supported by commercial experience in developing ML solutions.
• Strong knowledge of tools and libraries such as pandas, NumPy, scikit-learn, XGBoost, or PyTorch.
• Experience with cloud-based environments, preferably Microsoft Azure.
• Practical knowledge of MLOps and infrastructure tools such as MLflow, Airflow, Docker, and Kubernetes.
• Experience designing and maintaining scalable data, model-training, and deployment pipelines.
• Familiarity with Git, CI/CD, automated testing, and software engineering best practices.
• Proven ability to deliver ML solutions from initial concept to monitored production deployment.
• A proactive, independent, and well-organised approach.
• Strong communication skills and the ability to cooperate with technical and non-technical stakeholders.
• English proficiency at B2 level or higher.

Nice to have:

• Experience with pricing, price elasticity, recommendation systems, demand forecasting, optimisation, or the retail sector.

• Develop AI and ML products throughout their full lifecycle—from experimentation and prototyping to production deployment.
• Design end-to-end Machine Learning systems focused on scalability, reliability, performance, and maintainability.
• Build and automate data, training, testing, and deployment pipelines using MLOps and DevOps practices.
• Implement model monitoring, versioning, retraining, and performance-control mechanisms.
• Solve complex prediction, optimisation, recommendation, and decision-support problems.
• Provide technical direction and coordinate work across Machine Learning, Data Engineering, and DevOps.
• Define priorities, promote engineering standards, and support effective project delivery.
• Translate business challenges into practical, production-ready ML solutions.
• Improve infrastructure efficiency and optimise the cost of cloud-based workloads.
• Collaborate with Product Owners and business stakeholders from problem definition through implementation.