ML Ops Engineer
Description For ML Ops Engineer
Capco Poland is seeking an ML Ops Engineer to join their team. This role involves developing, deploying, and maintaining infrastructure for Machine Learning to support Data Science and Machine Learning Teams. The ideal candidate will focus on automating modeling processes, monitoring models in production environments, and managing the full ML lifecycle.
Key responsibilities include:
- Designing and developing pipelines for training and deploying ML models
- Automating and monitoring ML processes, including version control and model updates
- Collaborating with Data Science and Data Engineering teams to integrate ML models into production environments
- Building and maintaining systems for monitoring ML models to track performance and stability
- Optimizing cloud infrastructure (GCP) for ML deployments
- Troubleshooting issues related to performance and availability of ML models in production
- Ensuring CI/CD best practices for ML models
The tech stack includes Python, TensorFlow, PyTorch, MLflow, Kubeflow, Airflow, CI/CD, and GIT. Experience with tools like Spark, Hadoop, GCP, Pub/Sub, Big Query, Kafka, Juniper, Apache NiFi, Hive, Impala, and Cloudera is a plus.
Capco offers a flexible work environment with remote options, ongoing learning opportunities, and a flat, non-hierarchical structure. They provide multiple employee benefits packages, access to business courses, and opportunities to participate in charity events.
Join Capco to be part of an innovative work culture focused on creating lasting value for clients and employees in the financial services industry.
Responsibilities For ML Ops Engineer
- Design and develop pipelines for training and deploying ML models
- Automate and monitor ML processes, including version control and model updates
- Collaborate with Data Science and Data Engineering teams to integrate ML models into production environments
- Build and maintain systems for monitoring ML models to track performance and stability
- Optimize cloud infrastructure (GCP) for ML deployments
- Troubleshoot issues related to performance and availability of ML models in production
- Ensure CI/CD best practices for ML models
Requirements For ML Ops Engineer
- Experience with tools like: TensorFlow, PyTorch, MLflow, Kubeflow or similar
- Experience managing data pipelines (Airflow)
- Knowledge of DevOps practices (CI/CD, monitoring, automation)
- Proficiency in Python (with ML libraries) and Git
- Experience with model monitoring solutions (e.g. Prometheus, Grafana)
- Great communication skills in English (both written and spoken)
- Problem solver, team player with positive attitude
- University degree in computer science, mathematics, natural sciences, or similar field and relevant working experience
Benefits For ML Ops Engineer
- Employment contract and/or Business to Business - whichever you prefer
- Possibility to work remotely
- Multiple employee benefits packages (MyBenefit Cafeteria, private medical care, life-insurance)
- Access to 3.000+ Business Courses Platform (Udemy)
- Access to required IT equipment
- Paid Referral Program
- Participation in charity events e.g. Szlachetna Paczka
- Ongoing learning opportunities
- Flat, non-hierarchical structure
- Work culture focused on innovation and creating lasting value
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