AI Ops / ML Ops Engineer (m/f/d)

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.

Simon-Kucher is a global consultancy with more than 2,000 employees in 30+ countries.
Our sole focus is on unlocking better growth that drives measurable revenue and profit for our clients. We achieve this by optimizing every lever of their commercial strategy – product, price, innovation, marketing, and sales – based on deep insights into what customers want and value. With 40 years of experience in monetization topics of all kinds, we are regarded as the world’s leading pricing and growth specialist.

• What makes us special:
• How you will create an impact:
• Develop and maintain data architecture: create and manage robust data architectures that support high-volume, high-throughput SaaS applications, focusing on reliability and scalability.
• Drive faster and more reliable ML delivery by building robust MLOps foundations, including automated training pipelines, experiment tracking, and scalable model deployment.
• Accelerate AI product development by operationalizing LLMs end-to-end — from fine-tuning and evaluation to high-performance serving, monitoring, and embeddings workflows.
• Increase engineering velocity and system reliability by developing and maintaining unified CI/CD pipelines that ship ML and application code seamlessly.
• Enable scalable and cost-efficient AI workloads through well-architected cloud infrastructure across AWS/Azure/GCP.
• Improve performance and resilience of AI systems by managing Kubernetes clusters, optimizing autoscaling, and orchestrating GPU-heavy workloads.
• Enhance inference speed and portability by delivering highly optimized, secure Docker-based containers tailored for ML and LLM workloads.
• Strengthen data quality and model performance through well-designed ETL/ELT pipelines, streaming systems, feature store integration, and workflow orchestration.
• Ensure reliable and trustworthy AI operations by implementing comprehensive observability: logs, metrics, traces, and model/data drift detection.
• Reduce operational risk by embedding security and compliance best practices — IAM, RBAC, VPC design, secrets management, and encryption — into every layer of the stack.
• Increase automation, reduce manual toil, and support rapid experimentation by leveraging Python, Bash, and Terraform to script, codify, and automate infrastructure and ML workflows.

• You have previously owned end-to-end ML/AI infrastructure — from data ingestion and feature pipelines to training, deployment, and production monitoring.
• You enable data scientists to move faster by building self-service platforms, stable environments, and automated workflows that eliminate friction.
• You bring experience operating in high-availability SaaS environments (ideally B2B) with strict uptime, scalability, and security requirements.
• You have a track record of designing systems that scale globally across regions, workloads, and traffic patterns.
• You’re comfortable participating in incident response and on-call rotations, and you know how to stabilize and improve critical production systems.
• You think with a product mindset, focusing on customer value, reliability, and speed-to-market rather than technology for its own sake.
• You communicate clearly and collaborate effectively with engineering, data science, product, and security teams.
• You have a strong bias for automation — you eliminate manual operational toil by designing robust tooling and pipelines.