WHO WE ARE

AeroVect is transforming ground handling with autonomy, redefining how airlines and ground service providers around the globe run day-to-day operations. We are a Series A company backed by top-tier venture capital investors in aviation and autonomous driving. Our customers include some of the world’s largest airlines and ground handling providers. For more information, visit www.aerovect.com.

JOB DESCRIPTION

We’re looking for a skilled Software Engineer with a focus on ML Ops to design and implement robust data pipelines and infrastructure for AeroVect’s autonomous systems. In this role, you will take ownership of the entire machine learning lifecycle, from data collection and storage to labelling, training, and deployment of models. You will collaborate with robotics engineers and software developers to enable scalable, efficient, and high-quality machine learning solutions.

QUALIFICATIONS

• 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.

• 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.