What are your contributions to the team?

• Collaborate with experienced Machine Learning Engineers (MLEs) to develop and deliver machine learning (ML) solutions that meet the needs of Bombardier's organization.
• Assist in the deployment of ML models within the production data science environment, following best practices, pipeline controls, and maintaining clear documentation.
• Contribute to the development of tools for monitoring the machine learning life cycle and providing observability into the system's execution flow.
• Participate in designing, implementing, and optimizing maintainable and reusable ML services that enhance Bombardier's data and advanced analytics solutions.
• Assist in analyzing, processing, and modeling data, and help interpret the results to create actionable plans that positively impact Bombardier's operations.
• Work on configuring robust environments that handle data and microsystem connections, enabling ML workflows.
• Collaborate with the Artificial Intelligence (AI) team members, learning agile project management methodologies (e.g., Scrum, Kanban) and application lifecycle management practices (e.g., test automation, configuration management).
• Engage with users and business stakeholders to gather requirements and feedback to maximize value for the business units.
• Contribute to implementing continuous integration and deployment methods to automate quality assurance and the delivery of new functionality to ML solutions users.
• Provide support for troubleshooting/improving existing ML products.

How to thrive in this role?

• You have experience and a strong interest in analytics, data science, and computer engineering
• You are familiar with, and open to gaining experience, using cloud computing platforms.
• You have experience using Microsoft Azure’s advanced analytics tools (e.g., Azure Data Factory, Azure Databricks, Azure Machine Learning).
• You developed machine learning algorithms before.
• You are willing to learn about Docker and software packaging and ML production lifecycle.
• You are familiar with software architecture, and modular software development. Proficiency with Python is a must. Familiarity with Py Spark is a plus.
• You have familiarity with Linux scripting or Power Shell scripting.
• You are interested in agile project management and application lifecycle management, including familiarity with Azure Dev Ops or Gitlab.
• You possess strong problem-solving and debugging skills.