About the role
We are looking for a highly skilled Machine Learning Engineer between 1 to 5 years of working experience with robust programming expertise, a fundamental understanding of MLOps principles, and experience designing scalable, production-ready ML systems.
In this role, you will 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 Python package development and ensuring integration of ML models into production.
What will you do:
Software Engineering Architecture:
- 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.
Machine Learning Development:
- Apply a strong understanding of machine learning algorithms, especially tree-based models (e.g., LightGBM) to build time-series forecasting models.
- Design machine learning systems that are scalable and efficient.
- Engineer and optimize high-quality features for ML pipelines.
- Conduct model evaluation and tuning to improve performance.
MLOps:
- Build and maintain CI/CD pipelines for ML models and Python package releases.
- Design and build scalable data pipelines for ingestion and transformation while ensuring data quality, consistency, and efficiency.
- Deploy and serve models for batch and real-time inference (FastAPI, Flask).
Infrastructure Cloud Computing:
- Utilize Docker and Kubernetes to containerize and orchestrate machine learning workloads.
- Optimize resource allocation for large-scale ML training and inference.
Collaboration Mentorship:
- Work closely with data scientis