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Bombardier is a global leader in aviation, creating innovative and game-changing planes. Our products and services provide world-class experiences that set new standards in passenger comfort, energy efficiency, reliability and safety. We are a global organization focused on working together with a team spirit. Your boarding pass will include… Several conferences, including: Meet An Executive; Women Taking Flight. Learning more about Bombardier, including: Bombardier Products conference; Visits of the Bombardier sites; Bombardier Academy of Learning. Many social/networking opportunities, including: Volunteering; Networking for Success; 5 à 7, Potluck, and much more! During your internship, you will learn: Working with both traditional ML and Gen AI, including development, evaluation, and deployment; Data preparation, feature engineering, and model development using industry standard tools; GenAI workflows, including prompt engineering, embeddings, LLM fine tuning, and integration with business applications; How ML and GenAI systems are built and operated within cloud-based environments such as Databricks and Azure AI; MLOps and GenAI delivery best practices, including CI/CD, model monitoring, and versioning; Translating business requirements into data-driven and AI-powered solutions; Collaborating with cross-functional teams, including both technical and business stakeholders.
Job Responsibility:
Preparing and refining training datasets to support model development and evaluation
Applying feature engineering techniques to transform and enrich data
Developing and optimizing Python scripts for data processing, automation, and model experimentation
Collaborating with developers to build and deliver production ready AI solutions
Using cloud-based AI and data services within model development and deployment workflows
Deploying AI solutions and monitoring their performance in production environments
Troubleshooting and improving AI solutions built on Databricks and Azure
Prototyping and assessing machine learning models for a variety of use cases
Using DevOps and MLOps best practices for CI/CD, testing, and version control of ML solutions
Requirements:
Enrolled in an undergraduate or graduate program in computer science, data science, engineering, or a related applied science field
Strong proficiency in Python scripting
Curiosity and willingness to learn new tools and approaches in AI and ML engineering
Collaborative team player
Autonomy and ownership in your work
Proactive problem-solver who contributes ideas and solutions