Senior AI/ML Engineer – Applied AI Lead

David Kennedy Recruitment is collaborating with a global, fast-growing company to hire a Senior AI/ML Engineer – Applied AI Lead focused on machine learning solutions production and robust MLOps practices.

• 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

• 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