MLOps Engineer: Efficient Machine Learning Process Optimization
1 month ago
About the Role
We are seeking a highly skilled MLOps Engineer to join our team at GetInData | Part of Xebia. As an MLOps Engineer, you will play a crucial role in streamlining machine learning project lifecycles by designing and automating workflows, implementing CI/CD pipelines, ensuring reproducibility, and providing reliable experiment tracking.
As a key member of our team, you will collaborate with stakeholders and platform engineers to set up infrastructure, automate model deployment, monitor models, and scale training. You will possess a wide range of technical skills, including knowledge of orchestration, storage, containerization, observability, SQL, programming languages, cloud platforms, and data processing.
The ideal candidate will have expertise in various ML algorithms and distributed training in environments like Spark, PyTorch, TensorFlow, Dask, and Ray. You will be essential for optimizing and maintaining efficient ML processes in organizations.
Key Responsibilities
- Collaborate with Platform Engineers to set the infrastructure required to run MLOps processes efficiently
- Implement ML workflows and automate CI/CD pipelines
- Automate model deployment and implement model monitoring
- Collaborate with Platform Engineers to implement backup and disaster recovery processes for ML workflows, especially models and experiments
- Collaborate with stakeholders to understand the key challenges and inefficiencies of Machine Learning project lifecycles within the company
- Stay up-to-date with the latest trends and advancements in data engineering and machine learning
Requirements
- Proficiency in Python, Bash, and PowerShell
- Experience with orchestration and scheduling tools like Airflow, Prefect, and Dagster
- Knowledge of cloud services (Azure, AWS, GCP)
- Experience with frameworks like Databricks
- Familiarity with tools like MLflow, W&B, and Neptune AI from the operations perspective
- Experience with containerization technologies like Docker and basic knowledge of container orchestration platforms like Kubernetes
- Understanding of continuous integration and continuous deployment (CI/CD) practices, as well as experience with related tools like GitHub Actions or GitLab CI
What We Offer
- A competitive salary of $120,000 - $150,000 per year
- A dynamic work environment with international projects
- A flat structure and small teams
- Private healthcare and sports subscription
- Free coffee, gym, bike parking, and playroom
- In-house trainings and startup atmosphere
Location
Please note that we do not disclose specific locations due to security reasons.
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