Informacje o stanowisku

Salary brackets:

About role

MLOps Engineer is responsible for streamlining machine learning project lifecycles by designing and automating workflows, implementing CI/CD pipelines, ensuring reproducibility, and providing reliable experiment tracking. They collaborate with stakeholders and platform engineers to set up infrastructure, automate model deployment, monitor models, and scale training. MLOps Engineers possess a wide range of technical skills, including knowledge of orchestration, storage, containerization, observability, SQL, programming languages, cloud platforms, and data processing. Their expertise also covers various ML algorithms and distributed training in environments like Spark, PyTorch, TensorFlow, Dask, and Ray. MLOps Engineers are essential for optimizing and maintaining efficient ML processes in organizations.

Responsibilities

• Collaborating with Platform Engineers to set the infrastructure required to run MLOps processes efficiently
• Implementing ML workflows / automating CI/CD pipelines
• Automating model deployment and implementing model monitoring.
• Collaborating with Platform Engineers to implement backup and disaster recovery processes for ML workflows, especially models and experiments
• Collaborating with stakeholders to understand the key challenges and inefficiencies of Machine Learning project lifecycles within the company
• Keeping abreast of the latest trends and advancements in data engineering and machine learning

What we offer:

• Salary: 100 - 160 PLN net + VAT/h B2B (depending on knowledge and experience)
• 100% remote work
• Flexible working hours
• Possibility to work from the office located in the heart of Warsaw
• Opportunity to learn and develop with the best Big Data experts
• International projects
• Possibility of conducting workshops and training
• Certifications
• Co-financing sport card
• Co-financing health care
• All equipment needed for work

Requirements:

• Proficiency in Python
• Hands-on experience with Google Cloud Platform (GCP)
• Familiarity with tools such as MLflow and Vertex AI
• Knowledge of DevOps practices and tools, including Terraform
• Experience with docker and Kubernetes
• Understanding of continuous integration and continuous deployment (CI/CD) practices, as well as experience with related tools like GitHub Actions or GitLab CI

Warszawa - Oferty pracy w okolicznych lokalizacjach