Build the teams that take AI into production.

Location: Zurich OR Fribourg (Switzerland) Work Model: Hybrid – 3 days in office Contract Type: Permanent Responsibilities Design, develop, test, and maintain AI/ML infrastructure within a scalable microservices architecture running on Kubernetes. Build and maintain high-quality, secure, and reliable DevOps pipelines and Helm charts Work across the backend stack, integrating event-driven systems (Kafka), gRPC services, and REST APIs Develop and optimize data pipelines using modern data engineering tools (e.g., Spark) Manage ML lifecycle processes using tools such as MLflow Contribute to architectural decisions to improve scalability, performance, and system reliability Support deployment and monitoring of ML models in complex production environments, including isolated (air-gapped) setups with varying hardware constraints (CPU/GPU). Ensure platform reliability and robustness in customer-deployed Kubernetes environments Maintain high security and compliance standards aligned with industry best practices (e.g., ISO 27001 Requirements Degree in Computer Science, Engineering, or equivalent practical experience 5+ years of experience in AI/ML platform engineering or related roles Strong experience with Kubernetes, distributed systems, and data engineering technologies Hands-on experience with ML platforms and frameworks (e.g., MLflow, PyTorch, SparkML) Familiarity with modern data stack technologies (e.g., Spark, Delta Lake, TensorFlow, ONNX) Experience building clean, maintainable, and testable systems following modern software engineering principles Knowledge…

Location: Berlin OR Hessen (Germany) Work Model: Hybrid – 3 days in office Contract Type: Permanent Responsibilities Design, develop, test, and maintain AI/ML infrastructure within a scalable microservices architecture running on Kubernetes. Build and maintain high-quality, secure, and reliable DevOps pipelines and Helm charts Work across the backend stack, integrating event-driven systems (Kafka), gRPC services, and REST APIs Develop and optimize data pipelines using modern data engineering tools (e.g., Spark) Manage ML lifecycle processes using tools such as MLflow Contribute to architectural decisions to improve scalability, performance, and system reliability Support deployment and monitoring of ML models in complex production environments, including isolated (air-gapped) setups with varying hardware constraints (CPU/GPU). Ensure platform reliability and robustness in customer-deployed Kubernetes environments Maintain high security and compliance standards aligned with industry best practices (e.g., ISO 27001) Requirements Degree in Computer Science, Engineering, or equivalent practical experience 5+ years of experience in AI/ML platform engineering or related roles Strong experience with Kubernetes, distributed systems, and data engineering technologies Hands-on experience with ML platforms and frameworks (e.g., MLflow, PyTorch, SparkML) Familiarity with modern data stack technologies (e.g., Spark, Delta Lake, TensorFlow, ONNX) Experience building clean, maintainable, and testable systems following modern software engineering principles Knowledge…

Job Location – Amsterdam, Netherlands Experience (Years): 4+ years Pay Rate – TBN Start Date - ASAP 6 Months Contract – Renewable Tech Stack Python, SQL, PySpark, Azure, Databricks. Azure AI Search Requirements At least 4 years in ML/AI and Data Engineering Strong experience in Python, SQL, PySpark, and data transformation Experience with LLMs and Prompt Engineering Knowledge of MLOps and LLMOps Hands-on experience with Databricks (Unity Catalog, Jobs, Pipelines) Experience in NLP, Data Science, and Advanced Analytics Language Skills: Dutch Competencies Python Machine Learning Azure Machine Learning (ML) Generative AI

Senior AI / ML Engineer (MLOps & ML Platform) Location: Amsterdam, Netherlands Contract Duration: 6 months (renewable) Experience: 6–8 years About the Role We are looking for a Senior AI / ML Engineer to join our growing ML Engineering team. You will work closely with data scientists, engineers, and product managers to design, build, deploy, and operate robust machine learning systems that power key services across our Digital and Retail platforms. This role has a strong focus on MLOps and ML Platform development, helping scale and maintain production-ready ML workflows using modern cloud infrastructure and tooling. Key Responsibilities Design, develop, and maintain end-to-end ML pipelines for training, validation, deployment, and monitoring. Support scalable ML solutions across use cases such as recommendations, forecasting, and automation. Productionise data science models into reliable, scalable services. Build and operate ML services using Airflow, Azure ML, and FastAPI. Automate model deployment and lifecycle management using CI/CD pipelines (GitHub Actions, Azure DevOps). Improve reliability, observability, and performance of the ML platform. Implement monitoring, alerting, and model drift detection using tools like Azure Monitor, NewRelic, Grafana, and custom logging. Manage and evolve infrastructure using Terraform, Docker, and AWS Fargate. Collaborate cross-functionally with engineering, data, and product teams.…

Job Location – Zurich, Switzerland Experience (Years): 6+ years Pay Rate – TBN Start Date - ASAP 6 Months Contract – Renewable We are seeking an AI Engineer to collaborate with cross-functional teams in solving compliance and regulatory business challenges through innovative AI-powered solutions. You will contribute across the full lifecycle of AI model development—from problem definition to deployment—ensuring high-performance, scalable, and ethically responsible AI solutions. In this role, you will work on model development, training, fine-tuning, prompt engineering, and continuous improvement based on real-world performance and stakeholder feedback. You will design, build, test, and integrate AI-powered applications while adhering to best software engineering practices. Effective communication with both technical and non-technical stakeholders is essential to align project goals and deliverables. Responsibilities Collaborate with cross-functional teams to address compliance and regulatory business problems using AI-driven solutions. Contribute to the entire AI model lifecycle, including problem definition, development, training, fine-tuning, deployment, and ongoing improvements. Develop, test, and integrate AI-powered applications with strong adherence to software engineering best practices. Implement and refine RAG-based generative AI solutions and prompts. Continuously evaluate and improve model performance using real-world feedback. Communicate effectively with stakeholders across technical and business domains. Requirements 3–5 years of experience in…