AeroVect
AeroVect is a technology company that specializes in advanced automation for Ground Support Equipment (GSE) in the aviation industry. The company's flagship product, AeroVect Driver, is a highly scalable, OEM agnostic self-driving system designed to improve safety, productivity, and sustainability in ground handling operations. Trusted by leading airlines and handlers, AeroVect aims to revolutionize airside operations with its premier self-driving technology, built in Silicon Valley for the global aviation market.
Aviation • Autonomous Driving • Aerospace
Software Engineer - ML Ops
April 5
AeroVect
AeroVect is a technology company that specializes in advanced automation for Ground Support Equipment (GSE) in the aviation industry. The company's flagship product, AeroVect Driver, is a highly scalable, OEM agnostic self-driving system designed to improve safety, productivity, and sustainability in ground handling operations. Trusted by leading airlines and handlers, AeroVect aims to revolutionize airside operations with its premier self-driving technology, built in Silicon Valley for the global aviation market.
Aviation • Autonomous Driving • Aerospace
📋 Description
•AeroVect is transforming ground handling with autonomy. •We're looking for a skilled Software Engineer with a focus on ML Ops. •Design and implement robust data pipelines and infrastructure for AeroVect's autonomous systems. •Take ownership of the entire machine learning lifecycle, from data collection and storage to labelling, training, and deployment of models. •Collaborate with robotics engineers and software developers to enable scalable machine learning solutions.
🎯 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.
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