Job Description
Machine Learning Op's Analyst
100% Remote
6 Month Contract with the possibility of extension
**W-2 with Brooksource or c2c with employer
Key Responsibilities:
- Understand and work with ML requirements to develop, establish, and implement an end-to-end MLOps framework and Machine Learning Pipelines using Google Cloud Platform (GCP), Vertex AI, and other modern software tools.
- Automate ML pipelines for critical functions including scheduling, monitoring, status notification, and resiliency. This includes setting up re-training and monitoring pipelines with multiple criteria in GCP Vertex AI.
- Collaborate with analysts, data engineers, data scientists and visualization developers to ensure ML pipeline development is consistent with end-to-end solution delivery.
- Write robust, maintainable code with focus on meeting General Mills’ documented standards and best practices that could be moved to production without any large no of bugs/issues
- Support Object oriented programming and design patterns (in Python) to build code using reusable package/libraries development for streamlining ML workflows.
- Implement ML pipeline orchestration and configuration using Kubeflow and manage DAG/workflow orchestration using Airflow/Cloud Composer.
- Create and improve documentation for development and operation of ML workflows.
- Investigate, troubleshoot, and resolve production issues, providing support for ML models throughout their entire lifecycle from development to maintenance.
- Participate in code review and provide support and guidance to other engineers on the team.
- Participate in agile ceremonies and project management requirements.
- Research and operationalize new technologies and processes to scale MLOps and recommend best practices on new platforms and services.
Qualifications:
- Python: Significant experience in advanced Python programming, including a strong understanding of Object-Oriented Programming (OOPs) concepts and experience developing applications. Expertise with the Python ML ecosystem (pandas, scikit-learn, etc.).
- Cloud Platform (GCP): Significant experience developing ML pipelines in the cloud, with professional, hands-on experience with Google Cloud Platform (GCP), Vertex AI, and other core GCP services.
- Data Transformation: Expertise in data transformation and manipulation using BigQuery/SQL.
- Orchestration Frameworks: Professional experience with orchestration tools, specifically Airflow/Cloud Composer and Kubernetes/Kubeflow.
- Containerization: Professional experience with Docker containers.
- CI/CD and Version Control: Strong understanding and practical experience with CI/CD tools and processes, specifically using GitHub for version control and branching.
- Agile Methodology: Proven experience working within Agile software development environments. We use Azure Dev Ops (ADO) for projects.
- Communication: Strong verbal and written communication skills including the ability to interact effectively with colleagues of varying technical and non-technical abilities.
- Domain Knowledge: An understanding of the Consumer-Packaged Goods (CPG) industry is beneficial.