Senior AI/ML Engineer – Applied AI Lead

Job description

David Kennedy Recruitment is working with a global, fast-growing company that is seeking to onboard a Senior AI/ML Engineer – Applied AI Lead to join their team. The role focuses on taking machine learning solutions from development through to production, establishing robust MLOps practices and delivering AI use cases with measurable business impact. Position: Senior AI/ML Engineer – Applied AI Lead Location: Tallin/Limassol/Warsaw Work model: On-site in Tallin/Limassol, Hybrid in Warsaw Employment type: Full-time DUTIES AND RESPONSIBILITIES: Design, develop and productionise machine learning models across training, validation, deployment, monitoring and retraining Lead AI use cases including client lifetime value, churn prediction and fraud or abuse detection Build and establish robust MLOps practices, including deployment pipelines, CI/CD, environment promotion and model lifecycle management Implement model monitoring frameworks covering performance, data drift, data quality and business impact Establish retraining and escalation strategies for production models Implement model explainability and transparency using SHAP, feature attribution and other appropriate interpretability techniques Define and enforce best practices around model governance, documentation, versioning and auditability Collaborate with Data Engineering on data pipelines, feature engineering, reproducibility and scalable data foundations Partner with Product, Risk, Commercial and other stakeholders to translate business problems into pragmatic AI solutions Drive continuous improvement through monitoring insights, feedback loops and model retraining Mentor team members and promote best practices in production AI, MLOps and applied machine learning delivery REQUIREMENTS: 5–8+ years of experience building and deploying machine learning models in production environments Strong Python programming skills and solid software engineering fundamentals Strong understanding of machine learning concepts, model evaluation and feature engineering Practical understanding of production ML considerations including data leakage, drift and model stability Hands-on experience with Spark / PySpark and large-scale data processing Experience with MLflow or similar ML lifecycle tools Experience building and maintaining CI/CD pipelines , preferably with GitHub Actions Strong SQL skills and experience working with large, complex datasets Proven ability to deliver AI/ML solutions with measurable business impact Experience with model deployment, monitoring, drift detection and retraining strategies Strong communication skills with both technical and non-technical stakeholders Ability to balance MVP delivery speed with production robustness in evolving environments OFFER: Attractive remuneration package based on qualifications and experience Employee Training & Development programme Multiple team and group events Birthday and loyalty benefits And much more!