Software Engineer/ ML Engineer

About the role

We are looking for a highly skilled Machine Learning Engineer between 1 to 5 years of working experience with strong 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 scientists to integrate ML models into production.
• Contribute to internal ML documentation and knowledge-sharing sessions.
• Conduct code reviews and technical mentorship to other junior engineers.
• Lead best practices in code quality, testing, and ML governance.

What should you have:

• 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).

Preferred Qualifications:

• Time-Series Forecasting: Experience in designing and implementing time-series forecasting models.
• Open-Source Contributions: Experience in developing, maintaining, or contributing to open-source libraries.
• Model Explainability: Hands-on experience with SHAP or LIME for interpretability.
• CI/CD: Experience with CI/CD tools (GitHub Actions, Azure DevOps).
• Model Deployment: Knowledge about deploying models via REST APIs, Flask, or FastAPI.
• Real-Time Machine Learning: Knowledge of low-latency ML systems.