Informacje o stanowisku

Do you have experience working with machine learning models in production environments and want to develop in ambitious projects? We are looking for you!
For our client, we are looking for an MLOps Engineer to join a team responsible for deploying, monitoring, and maintaining machine learning models in a cloud environment.

Requirements:

• Documented experience in implementing and monitoring machine learning models in production environments,
• Minimum of 5 years experience working with Docker, Kubernetes, Helm, and CI/CD pipelines,
• At least 5 years of experience with monitoring tools such as Prometheus, Thanos, or Grafana,
• Familiarity with model monitoring tools such as Arize, Evidently AI, or Alibi Detect,
• Experience in conducting A/B tests and working with service mesh software (e.g., Istio),
• Proficiency in using platforms like Kubeflow or OpenDataHub for deploying and managing models,
• Very good understanding of infrastructure monitoring concepts and best practices in this area,
• Excellent problem-solving skills and diagnosing complex technical issues,
• Experience working with cloud platforms (AWS OR Google Cloud Platform OR Azure),
• Knowledge of scripting languages and automation (e.g., Bash, Python).
• Proficiency in English enabling fluent communication both verbally and in writing.

We offer:

• paid leave
• Work in an international environment - permanent - all work in an English-speaking environment

Responsibilities:

• Deploying machine learning models to production environments,
• Monitoring the performance of models in real-time,
• Creating and maintaining CI/CD pipelines,
• Configuring and managing Docker containers and Kubernetes clusters,
• Setting up and expanding system monitoring using tools like Prometheus, Thanos, Grafana,
• Implementing A/B testing and integrating with service mesh (e.g. Istio),
• Working with model management platforms such as Kubeflow and OpenDataHub,
• Automating tasks using scripts (e.g. Bash, Python),
• Resolving technical issues and optimizing system performance,
• Collaborating with Data Science, DevOps, and engineering teams to ensure deployment stability.

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