Chief Machine Learning Engineer

Recruiting a Chief Machine Learning Engineer requires a thorough understanding of the role. The following is a very general summary, which should be adapted to your specific context.
(Reporting to Head of AI Factory)

The Chief Machine Learning Engineer is the technical and operational leader responsible for overseeing the engineering of machine learning systems and ensuring their large-scale industrialization. Specialized in designing, optimizing, and deploying robust ML architectures, they lead a team of ML engineers and data engineers to transform data science models into high-performance, scalable, and maintainable production solutions. They collaborate in agile mode with product teams that define priorities through backlogs, while ensuring the technical excellence of ML systems.

Responsibilities and Missions

1. Architect and Optimize Machine Learning Systems

Design scalable ML architectures for model deployment in production.
Optimize processing pipelines (feature engineering, preprocessing, inference).
Develop infrastructure solutions for model training and serving.
Ensure performance and reliability of ML systems in production.

2. Industrialize Data Science Models

Collaborate with data scientists to operationalize their models.
Implement complete MLOps pipelines (training, validation, deployment, monitoring).
Automate CI/CD processes for ML models.
Optimize infrastructure costs while maintaining performance.

3. Collaborate in Agile Mode with Product Teams

Work with Product Owners who manage the backlogs of ML engineers.
Translate product requirements into technical ML solutions.
Participate in agile ceremonies to align technical developments.
Ensure technical constraints are considered in planning.

4. Ensure Quality and Maintainability of Systems

Implement automated tests for ML pipelines.
Define quality standards for code and infrastructure.
Document ML architectures and processes.
Ensure security and compliance of systems (GDPR, etc.).

5. Manage and Grow the ML Engineering Team

Recruit and train ML engineers and data engineers.
Structure the team by expertise domains (MLOps, infrastructure, feature engineering).
Mentor team members on ML engineering best practices.
Promote a culture of technical excellence.

6. Innovate and Anticipate Technological Advances

Monitor latest advancements in ML engineering (new frameworks, architectures).
Evaluate new approaches for model optimization.
Lead technical PoCs to test innovative solutions.
Participate in technical conferences to stay updated.

Examples of Concrete Achievements

Architected a model serving system reducing latency by 60% while improving scalability.
Implemented a complete MLOps pipeline, reducing model deployment time by 70%.
Optimized the training infrastructure, cutting costs by 40% while maintaining performance.
Developed a monitoring system for production models, improving anomaly detection by 50%.
Automated the feature engineering pipeline, reducing processing times by 35%.