Applied AI Engineer - Temporary Position

Mobileye is looking for an Applied AI Engineer to join the MECI Interfaces team within the Software Engineering organization. The MECI Group enables Mobileye’s algorithmic development flow by building scalable CI, build, test, and AI-driven tooling that supports the full development lifecycle. Our platforms allow Mobileye to scale efficiently and increase development velocity, while maintaining high standards of reliability, collaboration, and developer well-being.

As an Applied AI Engineer, you will be at the forefront of integrating Large Language Models (LLMs) and agentic workflows into our internal platforms. Your primary goal will be to solve complex problems across the Software Development Life Cycle (SDLC) by building intuitive, AI-powered tools that directly support our developers and internal users. This role offers the unique opportunity to both expand the intelligence of our existing developer tools and architect entirely new workflows from the ground up in a highly technical, fast-paced environment.

What will your job look like?

Design, develop, and deploy AI-driven internal tools and agentic workflows that accelerate the SDLC and reduce friction for Mobileye developers.

Expand the capabilities of existing platforms by embedding smart, context-aware LLM features.

Identify high-impact bottlenecks across the engineering organization and build zero-to-one AI solutions to solve them.

Architect robust orchestration layers around LLMs, focusing on practical implementations of custom system "skills," tool calling, Model Context Protocol (MCP) integrations, memory management, and Retrieval-Augmented Generation (RAG).

Maintain a framework-agnostic approach, rapidly evaluating and adopting the most effective AI models, APIs, and open-source techniques as the landscape evolves.

All you need is:

B.Sc. in Computer Science, Software Engineering, or a related technical field.

4+ years of hands-on software engineering experience, with a strong focus on Python development.

Proven experience building and deploying production LLM applications, including agentic. workflows, tool calling, and context management.

Nice to Have:

Deep understanding of CI/CD pipelines, DevOps practices, and automated build/test systems.

Experience with cloud infrastructure and modern deployment architectures.

Familiarity with Git and GitLab, backed by practical experience in automating developer workflows.