Machine Learning Operations Engineer
fgf brands
- Toronto, ON
- Permanent
- Temps-plein
- FGF believes in Home Grown Talent, accelerated career growth with leadership training. Unleashing Your Potential
- Competitive Compensation, Health Benefits, & a generous flexible medical / Health spending account
- RRSP matching program
- Tuition reimbursement
- Discount program that covers almost everything under the sun - Restaurants, gyms, shopping etc.
- Design and build modern web applications (front-end and back-end) using technologies like Python/.NET, React/Angular, JavaScript/TypeScript, etc.
- Develop RESTful APIs and backend services to support data processing and ML integration.
- Work with Azure services (App Services, Functions, Blob Storage, etc.) to build cloud-native solutions.
- Ensure robust CI/CD pipelines and DevOps best practices are in place for both app and ML model deployments.
- Help deploy machine learning models across environments—cloud, edge, and hybrid setups.
- Use Docker or other container tools to package and deploy ML solutions reliably.
- Automate ML workflows and integrate them into production-ready software systems.
- Assist with the setup and integration of development boards (e.g., NVIDIA Jetson).
- Connect sensors, cameras, and IoT devices for real-time data collection and processing.
- Support system optimization for performance and reliability in edge applications.
- Work closely with data scientists, engineers, and stakeholders to turn prototypes into scalable software products.
- Document architecture, workflows, and infrastructure clearly to support team handoffs and maintenance.
- Support networking and system configuration for integrated hardware-software solutions.
- Education in computer science and electrical engineering with minimum 5 years of experience in related roles or similar technical field of study.
- Strong experience with python, .NET, front-end frameworks (React, Angular, etc.), REST APIs.
- Comfortable working with Azure services and setting up cloud infrastructure.
- Solid grasp of containerization using Docker; familiarity with CI/CD tools like GitHub Actions or Azure DevOps.
- Bonus: ML familiarity, especially in integrating with PyTorch/TensorFlow models.
- Experience working with edge devices like Jetson Nano, Raspberry Pi, or other embedded platforms.
- Understanding of system-level tuning, hardware troubleshooting, or computer vision camera setups.
- A relentless drive to find elegant, scalable solutions to complex problems.
- Strong communication skills and a commitment to teamwork.