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 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.

📌 Software Engineer/ ML Engineer (Bengaluru)
🏢 o9 Solutions
📍 Bengaluru