Mission

Responsibilities

• Develop tools and frameworks for model training, tuning, and evaluation, ensuring seamless infrastructure integration

• Partner with Data Scientists and Engineers to bring AI solutions and Laboratories to production

• Supervise, review and validate pull requests, deployment, and development flows in the AI team

• Drive continuous improvements in MLOps processes and tools

• Sponsor the creation of new software and ML standards in the AI team

• Ensure the quality of your own and your teams' developments

• Interact with other functions and agree on data exchange protocols and architecture design (DevOps, Front End, Data Engineering, Back End, …)

• Ensure adherence to best practices in ML model governance and compliance

• Keep up to date with the latest trends in AI and MLE

Your profile

Profile

• >4 years' experience in a Machine Learning Engineer or Data Scientist role

• >4 years' experience with production-level Python code, strong programming skills. Bonus: having contributed to AutoML libraries, SaaS AI products.

• >3 years' experience in machine-learning methodologies (Machine Learning and Deep Learning)

• Familiarity with cloud-based infrastructure and services (AWS preferred)

• Experience with containerization and orchestration technologies such as Docker and Kubernetes

• Experience with version control and CI/CD systems (GitHub Actions preferred)

• Versed in the industry-standard MLE stack: MLflow, Airflow, DBT, REST APIs

• Experience communicating technically, at a level appropriate for the audience

• Ability to work in a fast-paced environment and learn quickly

• Enthusiasm for the latest advances in Generative AI

• (Bonus) Cloud certifications (preferably AWS)

• (Bonus) Background in product or AI SaaS companies

• (Bonus) Experience with GenAI stack: OpenAI, Claude, Langfuse, GenAI APIs, Langchain, PyTorch

• (Bonus) Proven track record of applying LLMs to improve their personal productivity

Who are we?

What sets us apart?

• We are an internationally diverse team that supports one another. 
• We develop high-quality software and thus create sustainable added value for our customers. 
• We live in a feedback culture so that we can constantly reflect and improve. 
• We enjoy new technologies and love to learn new things and grow with them. 
• We give freedom for further training because lifelong learning is important to us.