Senior Machine Learning Engineer (Applied AI & MLOps Lead)

Salve.Inno Consulting•Capon Bridge, WV

21h

About The Position

We are looking for an experienced Senior Machine Learning Engineer to join a fast-growing international organisation and take ownership of the development, deployment and continuous improvement of production-ready AI solutions. This position combines hands-on machine learning engineering, MLOps architecture and technical leadership, with a strong focus on delivering scalable AI applications that generate measurable business value. You will lead the implementation of machine learning solutions across areas such as customer lifetime value prediction, churn analysis and fraud detection, ensuring models are reliable, explainable and effectively maintained throughout their lifecycle. Working closely with Data Engineering, Product, Risk and Commercial teams, you will help establish robust MLOps practices, improve model governance and drive the adoption of applied AI across the organisation.

Requirements

5-8+ years of professional experience developing, deploying and maintaining machine learning models in production environments.
Advanced Python programming skills and strong software engineering fundamentals.
Solid understanding of machine learning algorithms, model evaluation techniques, feature engineering and predictive modelling.
Practical experience addressing production ML challenges, including data leakage, model drift, data quality and model stability.
Hands-on experience with Apache Spark or PySpark for distributed computing and large-scale data processing.
Experience using MLflow or comparable machine learning lifecycle management platforms.
Proven experience designing, implementing and maintaining CI/CD pipelines, preferably using GitHub Actions.
Advanced SQL skills and experience working with large, complex datasets.
Strong understanding of model deployment, monitoring, drift detection, retraining and production maintenance.
Experience implementing model explainability and interpretability techniques, including SHAP or similar approaches.
Demonstrated ability to deliver AI/ML solutions that generate measurable commercial or operational improvements.
Understanding of model governance, versioning, documentation and reproducibility requirements.
Ability to design scalable, reliable and maintainable machine learning architectures.
Strong analytical thinking and problem-solving skills.
Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
Experience collaborating with cross-functional teams, including Data Engineering, Product, Risk and Commercial.
Ability to balance fast-paced development and MVP delivery with robust production engineering practices.
Leadership mindset with the ability to mentor colleagues, influence technical decisions and drive engineering best practices.
Proactive, independent and results-oriented approach to delivering AI solutions.

Responsibilities

Design, develop, deploy and maintain machine learning models throughout their lifecycle, including training, validation, production deployment, monitoring and retraining.
Lead the development and implementation of applied AI solutions for customer lifetime value prediction, churn modelling, fraud detection and abuse prevention.
Establish and optimise MLOps frameworks, including automated deployment pipelines, CI/CD processes, environment promotion and model lifecycle management.
Develop monitoring systems to track model performance, data drift, data quality and measurable business outcomes.
Define and implement effective model retraining strategies, escalation procedures and continuous improvement processes.
Apply model explainability techniques, including SHAP and feature attribution, to improve transparency and interpretability.
Establish best practices for model governance, documentation, version control, reproducibility and auditability.
Collaborate with Data Engineering teams to develop scalable data pipelines, feature engineering processes and reliable data infrastructure.
Work with Product, Risk, Commercial and other stakeholders to translate business challenges into practical, scalable AI solutions.
Analyse production model performance and use monitoring insights to improve model accuracy, reliability and business impact.
Ensure machine learning solutions meet production standards for stability, scalability, maintainability and operational efficiency.
Provide technical guidance and mentorship to engineering colleagues, promoting best practices in applied machine learning and MLOps.
Balance rapid MVP development with long-term production reliability and scalability.