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Machine Learning Engineer: Scalable Solutions
1 week ago
At o9 Solutions, we're dedicated to digitally transforming planning and decision-making for enterprises worldwide. Our cutting-edge AI-powered platform, the o9 Digital Brain, enables global businesses to capture millions and billions of dollars in value leakage. By integrating siloed planning capabilities, we empower our clients to reduce waste, drive better outcomes, and make a positive impact on the planet.
We're seeking a highly skilled Machine Learning Engineer with 1 to 5 years of experience in designing scalable, production-ready ML systems. In this role, you'll be responsible for building robust machine learning models, streamlining MLOps workflows, and optimizing performance across the entire ML lifecycle. A key focus will be on developing and releasing internal Python packages for ML operations, evaluating and designing scalable ML system architectures, and refactoring existing codebases for scalability and performance.
Key Responsibilities:
- Write clean, modular, and efficient object-oriented Python code following best practices.
- Develop, maintain, and release internal Python packages for ML operations.
- Evaluate and design scalable ML system architectures, balancing performance, maintainability, and scalability.
- Refactor and optimize existing codebases for scalability and performance.
- FOLLOW Git workflow best practices, implement testing strategies, and ensure long-term code maintainability.
Requirements:
- Experience: 1 to 5 years in Machine Learning, Software Engineering, or related fields.
- Education: Bachelor's or Master's degree in Computer Science, AI/ML, or equivalent.
- Programming: Expert-level proficiency in Python, with strong coding skills.
- Machine Learning Algorithms: Fundamental understanding of various machine learning algorithms, including supervised and unsupervised techniques.
- MLOps: Good to have Hands-on experience with data pipelines, training/inference, deployment (batch/real-time), model retraining, testing (e.g. unit, regression), and version control.
- Infrastructure: Exposure/Experience with Docker.
- Version Control & Collaboration: Exposure/Experience with Git and Agile methodologies (e.g. Jira / Azure DevOps).
