AI & Machine Learning Engineer (M/F)

Data & AI Advisory Practice

TNP’s Data & AI Advisory practice supports clients in their data-driven transformation and AI industrialization journeys.

• Data governance & data strategy
• Machine Learning & AI solutions
• Data-driven business transformation

Your Role

As a Machine Learning Engineer, you will play a key role in industrializing AI solutions and delivering robust, scalable systems:

• Design and develop end-to-end Machine Learning solutions aligned with business needs
• Build and deploy scalable ML architectures across cloud and on-premise environments
• Develop and maintain data pipelines for training, evaluation, and model deployment
• Implement MLOps frameworks to industrialize the ML lifecycle in production
• Optimize model performance and set up monitoring and continuous improvement strategies
• Develop APIs and microservices to expose ML models to business applications
• Implement advanced feature engineering and data preprocessing pipelines
• Collaborate with Data Scientists to transform prototypes into production-grade solutions
• Contribute to best practices in testing, CI/CD, and deployment automation

Your Profile

• Master’s degree in Engineering, Computer Science, Applied Mathematics, or Data Science
• 3–5 years of experience in Machine Learning development and production deployment

Technical expertise

• Strong programming skills in Python
• Solid experience with ML frameworks (Scikit-learn, TensorFlow, PyTorch, Keras)
• Strong knowledge of MLOps tools (MLflow, Kubeflow, DVC) and ML-oriented DevOps practices
• Experience with cloud platforms (AWS SageMaker, Azure ML, GCP Vertex AI)
• Good command of containerization and orchestration (Docker, Kubernetes)
• Experience in API development (FastAPI, Flask) and databases (SQL / NoSQL)
• Familiarity with CI/CD pipelines, versioning, and model monitoring in production

Key strengths

• Strong analytical and problem-solving mindset
• Ability to work in cross-functional teams (Data, Tech, Business)
• Proactive, autonomous, and delivery-oriented
• Curiosity and adaptability in a fast-evolving ML ecosystem
• Professional proficiency in English