You are:
An experienced MLOps Engineer who thrives on building the backbone of production-grade ML systems. You enjoy bridging the gap between model development and production, creating scalable infrastructure, and empowering ML teams to ship models with confidence. You’re comfortable working across cloud services, containerized environments, and CI/CD pipelines—and you understand the importance of reproducibility, monitoring, and automation.
You will:
Design and implement infrastructure for ML model training, testing, deployment, and monitoring.
Collaborate with ML engineers and data scientists to streamline model operationalization and CI/CD integration.
Manage containerized environments (Docker, Kubernetes/ECS) and model serving infrastructure.
Monitor production ML systems for performance degradation, data drift, and retraining needs.
Use tools like MLflow to track experiments, manage model versions, and support model governance.
Automate workflows using tools like Airflow, Step Functions, or similar orchestration platforms.
Contribute to IaC (e.g., Terraform or CloudFormation) to ensure reproducible infrastructure deployments.
A degree in computer science, engineering, or a related field.3–6+ years of experience in DevOps, Cloud Engineering, or MLOps roles supporting machine learning teams.
Proficiency in Python and experience with ML frameworks (e.g., Scikit-learn, PyTorch, TensorFlow).
Hands-on experience with AWS services like S3, SageMaker, Lambda, Redshift, and ECR.
Expertise with containerization and orchestration tools (Docker, ECS, Kubernetes).
Familiarity with ML lifecycle tooling such as MLflow, DVC, or SageMaker Pipelines.
Solid understanding of CI/CD pipelines, Git workflows, and infrastructure-as-code.
Strong collaboration skills and a mindset of continuous improvement.
Fluency in English.
Nice to have
Experience with real-time model serving and streaming pipelines.
Knowledge of observability frameworks (Prometheus, Grafana, etc.).
Familiarity with security and compliance practices for ML systems.
Interest in emerging areas such as ML governance or responsible AI.
Benefits
Private health and life insurance
English lessons with a Native Speaker
Psychological support
MultiSport card
Lunch card - for employees who work in the office
Flexible working hours
Note: Flexible working hours and occasional work from home options in Better Collective help us achieve a proper work-life balance. We strongly believe in the magic of teamwork, though, so we come to the office at least three days a week to keep the team spirits high.
Application Deadline
We look forward to hearing from you and accept applications until the 5th of July
Please submit your CV and cover letter in PDF; only applications submitted in English will be considered.
Expected start date: as soon as possible.