AI Ops / ML Ops Engineer (m/f/d)
In Germany - Berlin | Bonn | Cologne | Frankfurt/Main | Hamburg | Munich
We are seeking an experienced AI Ops Engineer to contribute to designing and building scalable SaaS products within our AI Lab. In this role, you’ll combine deep technical expertise with strategic vision to build AI-powered products that will help transform our clients’ business models and enable their growth.
Simon-Kucher is at the forefront of innovation in driving commercial excellence, revamping business models, developing solutions and methodologies for unlocking better growth of our clients. Within AI Lab, we are developing cutting-edge large scale AI products to deliver sustained top-line impact for our clients.
Are you interested in working in a team of AI evangelists with a can-do attitude? Want to experience the dynamics of agile processes in open-minded teams? How about getting creative in a startup atmosphere with a steep development curve and flat hierarchies? And most importantly, do you want to make a difference? Then you've come to the right place.
What makes us special:
1. Advance your career with exciting professional opportunities in our thriving company with a startup feel
2. Innovate by transforming ideas into cutting-edge AI products, championing AI and Generative AI through creative experimentation to push boundaries and deliver transformative solutions.
3. Voice your unique ideas in a culture defined by our entrepreneurial spirit, openness, and integrity
4. Feel at home working with our helpful, enthusiastic colleagues who have great team spirit
5. Broaden your perspective with our extensive training curriculum and learning programs (e.g. LinkedIn Learning)
6. Speak your mind in our holistic feedback and development processes (e.g. 360-degree feedback)
7. Satisfy your need for adventure with our opportunities to live and work abroad in one of our many international offices
8. Enjoy our benefits, such as hybrid working, daycare allowance, corporate discounts, and wellbeing support (e.g. Headspace)
9. Unwind in our break areas where you can help yourself to the healthy snacks and beverages provided
10. See another side of your coworkers at our frequent employee events, World Meetings and Holiday Parties
How you will create an impact:
11. Develop and maintain data architecture: create and manage robust data architectures that support high-volume, high-throughput SaaS applications, focusing on reliability and scalability.
12. Drive faster and more reliable ML delivery by building robust MLOps foundations, including automated training pipelines, experiment tracking, and scalable model deployment.
13. Accelerate AI product development by operationalizing LLMs end-to-end — from fine-tuning and evaluation to high-performance serving, monitoring, and embeddings workflows.
14. Increase engineering velocity and system reliability by developing and maintaining unified CI/CD pipelines that ship ML and application code seamlessly.
15. Enable scalable and cost-efficient AI workloads through well-architected cloud infrastructure across AWS/Azure/GCP.
16. Improve performance and resilience of AI systems by managing Kubernetes clusters, optimizing autoscaling, and orchestrating GPU-heavy workloads.
17. Enhance inference speed and portability by delivering highly optimized, secure Docker-based containers tailored for ML and LLM workloads.
18. Strengthen data quality and model performance through well-designed ETL/ELT pipelines, streaming systems, feature store integration, and workflow orchestration.
19. Ensure reliable and trustworthy AI operations by implementing comprehensive observability: logs, metrics, traces, and model/data drift detection.
20. Reduce operational risk by embedding security and compliance best practices — IAM, RBAC, VPC design, secrets management, and encryption — into every layer of the stack.
21. Increase automation, reduce manual toil, and support rapid experimentation by leveraging Python, Bash, and Terraform to script, codify, and automate infrastructure and ML workflows.
About you:
22. You have previously owned end-to-end ML/AI infrastructure — from data ingestion and feature pipelines to training, deployment, and production monitoring.
23. You enable data scientists to move faster by building self-service platforms, stable environments, and automated workflows that eliminate friction.
24. You bring experience operating in high-availability SaaS environments (ideally B2B) with strict uptime, scalability, and security requirements.
25. You have a track record of designing systems that scale globally across regions, workloads, and traffic patterns.
26. You’re comfortable participating in incident response and on-call rotations, and you know how to stabilize and improve critical production systems.
27. You think with a product mindset, focusing on customer value, reliability, and speed-to-market rather than technology for its own sake.
28. You communicate clearly and collaborate effectively with engineering, data science, product, and security teams.
29. You have a strong bias for automation — you eliminate manual operational toil by designing robust tooling and pipelines.