About Jan
Hi 👋
My name is Muthu kamalan and welcome to my blog!
📧 Email: muthukamalan98@gmail.com
📱 Phone: +91 94868-72592
🎂 Born: April 27, 1998
📍 Location: Kovilpatti, Tamil Nadu, India
About Me
Machine Learning Engineer with 6+ years of experience building production-ready Machine Learning and MLOps solutions.
I enjoy designing end-to-end ML pipelines—from data ingestion and preprocessing to model training, deployment, and monitoring.
Beyond the technical side, I value clean architecture, collaboration, and continuous learning. I’m driven by the challenge of making machine learning systems not just accurate, but dependable, maintainable, and scalable.
Technology Stack
| Category | Technologies |
|---|---|
| Languages |   |
| OS & Terminal |   |
| Editors |    |
| Backend Development |     |
| Data Science |      |
| Machine Learning |     |
| LLM & AI |   |
| Cloud & Deployment |   |
| Observability |   |
| Testing |   |
| Automation |   |
| Infrastructure |  |
| Cloud |   |
| Version Control |    |
| Database |  |
| ETL & Workflow |    |
| Web Automation |  |
| Knowledge Management |  |
Professional Experience
💼 Experience
2024 – Present
Senior MLOps Engineer
Capgemini- Designed and maintained end-to-end ML workflows using Azure ML Studio.
- Automated CI/CD pipelines using Azure DevOps.
- Implemented MLflow tracking and model registry.
- Architected Feast Feature Store.
- Supported production credit-risk ML systems.
2023 – 2024
Associate MLOps Engineer
Ojcommerce- Built automated Amazon Buy Box pipelines.
- Developed Selenium + XGBoost pricing system.
- Managed Apache Airflow workflows.
- Improved revenue by 18%.
- Deployed CNN models using AWS SageMaker and Lambda.
2022 – 2023
Data Scientist
Math Company- Media Mix Modeling.
- Multi-Touch Attribution.
- Statistical Analysis.
- Built Tableau dashboards.
2020 – 2022
Database Administrator (IBM DB2)
Tata Consultancy Services
- Database administration and optimization.
- Built monitoring pipelines.
- SQL performance tuning.
- Operational analytics.
Education
| Institution | Duration | Program |
|---|---|---|
| The School of AI | 2023 – 2024 | Extensive Machine Learning Operations V5 |
| Scaler Academy | 2021 – 2022 | Data Science & MLOps |
| Sona College of Technology | 2016 – 2020 | B.E. Electrical & Electronics Engineering |
| Nadar Higher Secondary School | 2014 – 2016 | Higher Secondary |
| Nadar Higher Secondary School | 2013 – 2014 | High School |
Certifications
- Machine Learning in Production — DeepLearning.AI (Course ID: 5U6DSTWRUDIU)
- MLOps — Udemy
Senior MLOps Engineer — Capgemini
2024 – Present
- Designed and maintained end-to-end ML workflows using Azure ML Studio.
- Automated CI/CD pipelines using Azure DevOps.
- Implemented MLflow tracking, registry, and model versioning.
- Architected Feast Feature Store for feature versioning and reuse.
- Supported credit-risk model development using scikit-learn, XGBoost, LightGBM, pandas, and NumPy.
- Collaborated with Data Scientists to operationalize production ML systems.
Associate MLOps Engineer — Ojcommerce
2023 – 2024
- Built automated Amazon Buy Box data pipelines.
- Developed Selenium-based scraping systems with XGBoost pricing prediction.
- Orchestrated workflows using Apache Airflow.
- Improved revenue by 18% through automated pricing optimization.
- Automated furniture product classification and marketplace publishing.
- Trained distributed CNN models on AWS SageMaker and deployed ONNX models via AWS Lambda.
Data Scientist — Math Company
2022 – 2023
- Conducted statistical analysis on customer interactions.
- Worked on Media Mix Modeling (MMM).
- Built Multi-Touch Attribution models.
- Created Tableau dashboards for business insights.
Database Administrator (IBM DB2) — Tata Consultancy Services
2020 – 2022
- Managed database performance and optimization.
- Built metadata collection pipelines.
- Developed SQL Server utilization monitoring.
- Designed operational analytics pipelines.
Areas of Interest
- Machine Learning Engineering
- MLOps
- Deep Learning
- Distributed Training
- Feature Stores
- LLM Applications
- Production AI Systems
- Cloud-native ML
- Backend Engineering
- ML Infrastructure
“Building machine learning systems that are reliable, scalable, and production-ready.”