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# SI Engineer

- **Company:** Tesla
- **Location:** Berlin, Germany
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Continuous Integration, Github, PostgreSQL, Machine Learning, Node.Js, Next.js, Software Engineering, TypeScript, ReactJS, Retrieval-Augmented Generation, Large Language Models, Model Validation, Containerization, Kubernetes, Software Version Control, Docker
- **Published:** October 15, 2026
- **Apply:** https://www.arbeitsagentur.de/jobsuche/jobdetail/15158-2026286309-S

## About the Role

* 3+ years of software engineering experience with proven ability to deliver production applications both independently and in teams
 * Deep hands-on experience building production systems with LLMs - you've designed, deployed, and iterated on real AI-powered products independently, involving RAG pipelines and agentic frameworks. Experience with the Vercel AI SDK is a big plus
 * Strong full-stack engineering skills across modern web stacks (TypeScript, Node.js, React) with the ability to rapidly pick up new technologies
 * Solid understanding of evaluation and monitoring for AI systems - you know how to measure whether your AI is actually working and how to improve it
 * Strong business intuition: you identify what's worth building, prioritize ruthlessly, and ship solutions that move the needle. You're comfortable translating between technical and non-technical stakeholders and thrive at the intersection of engineering and business strategy
 * Experience with GitHub Actions, Docker, Kubernetes, and PostgreSQL. Ideally, you've set up CI/CD and containerized deployments from scratch
 * Self-directed and high-agency - you drive projects forward, own outcomes end-to-end, and don't wait for direction

## Description

* Collaborate directly with HR leadership, business partners, and cross-functional teams to identify high-impact opportunities and turn them into working software
 * Architect, develop, ship, and maintain AI-powered production systems that solve real HR problems with measurable bottom-line impact
 * Drive technical decisions on AI/ML architecture, model selection, and evaluation frameworks
 * Apply rigorous software engineering practices including version control, testing, CI/CD, and containerization
 * Mentor junior engineers and interns, and drive improvements for AI/ML development across the team