Software Engineer, ML Ops
Description For Software Engineer, ML Ops
AeroVect is an innovative Series A company that's revolutionizing the aviation industry through autonomous ground handling solutions. We're seeking a talented Software Engineer specialized in ML Ops to join our engineering team in Toronto. In this role, you'll be at the forefront of developing and maintaining the infrastructure that powers our autonomous systems.
As our ML Ops Engineer, you'll take complete ownership of the machine learning lifecycle, from initial data collection through to model deployment. You'll work with cutting-edge technologies and build scalable solutions that directly impact the efficiency of airport ground operations worldwide. Your expertise in data pipelines, infrastructure, and ML systems will be crucial in helping us deliver robust autonomous solutions to some of the world's largest airlines and ground handling providers.
The ideal candidate brings a strong foundation in software engineering with specialized experience in ML Ops or data engineering. You'll need to be proficient in Python and comfortable working with various data storage solutions and cloud platforms, particularly AWS. Your experience with containerization and orchestration tools like Docker and Kubernetes will be essential for deploying and managing ML systems at scale.
This is an exciting opportunity to join a well-funded startup that's making a real impact in the aviation industry. You'll be working with a cross-functional team of talented engineers and roboticists, solving complex challenges in autonomous systems. If you're passionate about machine learning infrastructure and want to be part of transforming how airports operate, this role offers the perfect blend of technical challenge and industry innovation.
Responsibilities For Software Engineer, ML Ops
- 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
- 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
Requirements For Software Engineer, ML Ops
- 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
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