Expert(e) en ML Ops

ENERCON, a world-renowned company based in Germany for over 40 years, positions itself as the undisputed leader in the design and manufacture of wind turbines. Our reputation is built on our unwavering commitment to excellence, the adoption of innovative direct-drive technologies, and strict adherence to quality standards. Additionally, we enter into long-term service contracts, thus consolidating our position as a global benchmark for reliability and performance.

With a product range covering from 2 MW to 6 MW, ENERCON is at the forefront of innovation in the wind industry, pushing the entire sector towards a promising future. To date, we have installed over 32,500 wind turbines worldwide, generating a total capacity of 61 GW.

Our North American headquarters is located in Montreal (Quebec), with service centers from coast to coast, as well as an accredited warehouse and training center in Boucherville (Quebec). ENERCON has a significant presence in the Canadian market since 2001, currently representing over 2,700 MW of installed capacity across eight provinces and territories in Canada.

At ENERCON in North America, we are committed to building a cleaner and more sustainable future, promoting the energy transition, and offering exciting career opportunities to those who share our vision. Explore our job opportunities and join us in shaping the future of renewable energy and contributing to a world powered by sustainable energy.

ENERCON is seeking a qualified ML Ops Expert to join our team and contribute to the sustainable and efficient use of machine learning (AI) within our organization. You will have the opportunity to collaborate with our global team of experts and work closely with our technical lead based in Canada.

• Guidelines and Support: Develop and provide expert support to promote the gold standard of AI, ensuring sustainability, performance, and traceability of ML outcomes.
• Framework Implementation: Create, implement, and monitor the machine learning operations framework to ensure continuity of knowledge and outcomes throughout the lifecycle of our assets.
• Cross-Functional Collaboration: Bridge the gap between data science and data engineering, ensuring the deployment and management of machine learning models.
• Model Management: Streamline the development, deployment, and management of machine learning models in production environments.
• Collaborative Solutions: Work with data scientists, DevOps teams, and stakeholders to create efficient and secure AI solutions.
• Maximizing Value: Ensure and maximize value creation from advanced analytics products.

Technical Skills:

• Proficiency with popular ML frameworks such as Databricks ML Flow, TensorFlow, PyTorch, or scikit-learn.
• Knowledge of continuous integration and deployment (CI/CD) practices for machine learning model pipelines.
• Familiarity with cloud services (e.g., AWS, Azure, GCP) for scalable ML infrastructure.
• Ability to design robust data pipelines and manage large datasets.
• Expertise in deploying ML models in production environments.