Applied AI Engineer II

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Learneo

WebsiteLinkedInAll Job Openings

501 - 1000 employees

📚 Education

⚡ Productivity

🤝 B2B

Education • Productivity • B2B

Learneo is a pioneering platform that focuses on building a collection of builder-driven businesses aimed at enhancing productivity and learning for everyone. By fostering an environment of decentralized entrepreneurial teams, it promotes innovation, collaboration, and the efficient delivery of educational resources. Committed to continuous growth, Learneo offers a wide range of educational tools and services, empowering individuals to learn and achieve their fullest potential in a dynamic and evolving online learning market.

📋 Description

• Collaborate with experienced data scientists and software engineers to gain insights into building scalable and efficient data pipelines, model training, and deployment systems. • Troubleshoot issues in the entire machine learning infrastructure, from Linux, Docker, and Kubernetes up to the highest levels of our ML stack. Resolve issues, improve system performance, and make our stack the best in the industry. • Assist in the design and development of on-premises MLOps solutions to support the delivery of machine learning models, and a seamless handover between research and productionization of ML artifacts. • Drive and uphold high engineering standards, bringing consistency to codebases encountered and ensuring software is adequately reviewed, tested, and integrated. • Optimize existing models for better performance and throughput. • Incorporate ML model training, validation, and evaluation settings in addition to traditional coding tests like unit and integration testing. • Build and maintain tools for deployment, monitoring, and operations. - Continuously refine and enhance CI/CD workflows to support the evolving needs of the machine learning infrastructure.

🎯 Requirements

• 3+ years of experience in MLOps or full stack Machine Learning - Good programming skills in a modern programming language (Python, Scientific Python Stack, Cuda). • Understanding of the MLOps life cycle and experience with MLOps workflows. • Experience with tools & practices of the trade, such as Kubernetes, GCP/AWS/Azure, CI/CD, common ML frameworks, and data management. • A keen interest in machine learning engineering and a willingness to explore how it can be scaled effectively. • Strong desire to learn and good communication skills, with an enthusiasm for collaborative problem-solving.

🏖️ Benefits

• Competitive salary and annual bonus • Medical coverage • Life and accidental insurance • Vacation & leaves of absence (menstrual, flexible, special, and more!) • Developmental opportunities through education & developmental reimbursements & professional workshops • Maternity & parental leave • Hybrid & remote model with flexible working hours • On-site & remote company events throughout the year • Tech & WFH stipends & new hire allowances • Employee referral program • Premium access to Quillbot