Software Engineer – ML Ops
| Company | AeroVect |
|---|---|
| Location | Toronto, ON, Canada |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | Bachelor’s, Master’s |
| Experience Level | Junior, Mid Level |
Requirements
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
- 2+ years of experience in software engineering, with a focus on ML Ops or data engineering.
- Proficiency in programming and scripting languages such as Python
- Familiarity with data storage solutions (e.g., S3, Hadoop, HDFS) and database systems (SQL and NoSQL).
- Experience with containerization (Docker) and orchestration (Kubernetes) for deploying ML systems.
- Knowledge of cloud platforms (AWS) and their machine learning services.
- Excellent problem-solving skills and attention to detail.
- Strong communication and collaboration skills, with the ability to work effectively in a cross-functional team environment.
Responsibilities
- Design, build, and maintain scalable data pipelines for collecting, processing, and storing large-scale structured and unstructured datasets.
- Develop tools and frameworks for efficient data labeling, annotation, and curation.
- Collaborate with software engineers to streamline model training workflows, ensuring reproducibility and scalability.
- Implement and optimize storage solutions for large datasets, ensuring accessibility and performance.
- Build and maintain CI/CD pipelines for machine learning models, enabling seamless integration and deployment into production systems.
- Develop monitoring and logging solutions to ensure the health and performance of deployed models.
- Optimize and automate training pipelines, including hyperparameter tuning and distributed training.
Preferred Qualifications
-
No preferred qualifications provided.