Mlops Engineer (CX)

MLOps Engineer (CX)

Our mission as a company providing IT services is to provide our clients all over the world with the best solutions. We manage to do this by analyzing the needs of our clients and matching them to the skills and aspirations of our employees. Therefore, one of our main motivations is to provide each Employee and Consultant with a satisfying experience. Joining us means being part of a community with diverse personalities. Start your adventure with act digital Polska !

Job Description

• Infrastructure Management: Architect and manage scalable cloud infrastructure workloads, including container orchestration and automated testing.
• Research Collaboration: Partner closely with data scientists and research teams to translate experimental models into robust, production-ready systems.
• DevOps Best Practices: Establish infrastructure as code, CI/CD pipelines, automated deployments, and comprehensive logging/monitoring.

Qualifications

• 5+ years of experience after completing higher education.
• Advanced Python Programming: Production Python experience with web frameworks (FastAPI, Flask), testing frameworks,
• Cloud Computing Expertise: Hands-on experience with major cloud platforms (AWS, GCP, or Azure), including Kubernetes services (EKS/GKE/AKS).
• Research Team Collaboration: Experience working with data science or research teams, effectively translating experimental code into production systems.
• Software Engineering: Strong foundation in version control, testing strategies, software architecture principles, async programming, and concurrent system design.
• Data Infrastructure: Design and implement scalable data infrastructure solutions leveraging distributed computing frameworks like Apache Spark or similar for large-scale data processing. Build and optimize data lake architectures to support analytics, ensuring high performance, reliability, and data governance across large datasets.
• ML experience not required, but you should know why you want to work in this field.
• English min B2.
• Nice to have
• ML libraries (PyTorch, scikit-learn, numpy).
• Production ML Pipeline Development: Design, build, and maintain end-to-end ML pipelines from data ingestion to model deployment and monitoring.
• ML Infrastructure: Experience with MLOps tools (MLflow, Kubeflow), container technologies (Docker, Kubernetes), inference engines (vLLM, SGLang), distributed computing (Ray.io), and data labeling platforms (Label Studio).
• Managed ML services (SageMaker, Vertex AI.