Senior AI Engineer

At JFrog, we're reinventing DevOps to help the world's greatest companies innovate – and we want you along for the ride. This is a special place with a unique combination of brilliance, spirit, and just all-around great people. Here, if you're willing to do more, your career can take off. And since software plays a central role in everyone's lives, you'll be part of an important mission. Thousands of customers, including the majority of the Fortune 100, trust JFrog to manage, accelerate, and secure their software delivery from code to production – a concept we call "liquid software." Wouldn't it be amazing if you could join us on our journey?

We are seeking for an experienced, hands-on Senior ML / AI Engineer to join the Generative AI applications platform group at JFrog and lead the implementation and integration of AI/ML solutions.

As a Senior ML / AI Engineer at JFrog you will...
Understand the challenges our users face and how to integrate and implement AI/ML solutions to solve those challenges
Work with stakeholders such as engineers, Product Management, and analysts to translate business problems to technical requirements and solutions
Design, develop, and implement POCs for ML models and GenAI solutions to address business needs and objectives
Provide technical guidance and mentorship to development teams on AI/ML technologies and practices
Build ML capabilities from ideation to production
To be a Senior ML / AI Engineer at JFrog you need…
5+ years of proven experience in AI / ML roles that include both research and production hands on work
5+ years of experience writing production-grade code in Python using modern paradigms such as Event Driven Architectures, microservices, and the like
Proficiency in building, training, running, and evaluating deep learning models using Hugging Face, Pytorch, Tensorflow, or similar frameworks
Proficiency in building production-grade Generative AI applications using techniques such as multi-agent architectures, RAG and fine tuning
Experience with the entire ML model lifecycle, from ideation all the way to productization
Experience with MLOps frameworks such as Amazon SageMaker, Vertex AI or similar for building end-to-end ML pipelines
Deep understanding of ML theory and the techniques to apply it in the real world, such as tuning, feature and error analysis, cross-validation, and over/underfitting
Bachelor's degree or higher in Computer Science or a similar computational discipline
Excellent problem-solving abilities and analytical skills
Strong communication and team collaboration skills