Machine Learning Engineer (m/f/d)

Your mission

About octonomy   
At octonomy, we’re not a typical AI startup. We’re building big — backed by one of Europe’s largest seed rounds (€20M) to scale our vision fast and sustainably. Our mission is clear: we transform companies with the most intelligent digital workforce, enabling a new era of growth.
Our AI octo-Workers automate business processes with high reliability, strong security, and zero hallucinations. Leading customers already trust us — and we’re growing rapidly across Germany and internationally.

Your Role
As Machine Learning Engineer (m/f/d), you will design and ship production-grade AI systems at the intersection of machine learning, large language models, and agentic workflows. This is not a research-only role — and not a generic model training role. You will take ownership of real AI systems that power automation in mission-critical business environments.

• Build end-to-end AI systems in production: Develop, deploy, andoperateML and LLM-based models that deliver measurable impact.
• Work on modern LLM architectures: Design and improve systems such as RAG pipelines, tool-using agents, and multi-step reasoning workflows.
• Own reliability and evaluation: Set up robust evaluation frameworks, monitoring, and guardrails to ensure consistent performance and minimal hallucinations.
• Develop scalable ML infrastructure: Improve training workflows, deployment pipelines, and automation (CI/CD for ML, reproducibility, model lifecycle).
• Shape knowledge-centric AI solutions (future direction): Contribute to building structured knowledge bases, potentially involving ontology-driven knowledge graphs.
• Lead technical projects end-to-end: Drive architecture decisions,establishbest practices, and mentor others as we scale our AI engineering organization.

Your profile

• Strong experience shipping ML systems: Several years of experience building and deploying models in real-world production environments.
• LLM & agentic systems expertise: Hands-on work with large language models, RAG setups, orchestration frameworks, or agentic workflows.
• Research mindset and culture: Experience from R&D, publications, or advanced academic work is a plus — but most importantly, you work systematically: experimentation, documentation, and best practices.
• Solid software engineering fundamentals: Proficiency in Python and ML frameworks, with clean, tested, maintainable code.
• ML Ops and production ownership: Comfortable with Docker, CI/CD, monitoring, and operating models beyondinitialtraining.
• Leadership and project ownership: Proven ability to lead complex technical projects end-to-end (Staff/Principal mindset is a strong plus).

Why us?

• A role with real ownership: you’ll directly improve the day-to-day experience of the team 
• A fast-moving environment where quality and pragmatism matter 
• Close collaboration with experienced IT Operations and a global team 
• Flexible working hours that fit around your studies 
• The opportunity to grow into broader IT Ops topics (tooling, processes, security basics)