Senior AI/ML Engineer – Applied AI Lead

David Kennedy Recruitment•Capon Bridge, WV

13h•Onsite

About The Position

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.

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

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