I'm an experienced Big Data and Machine Learning Engineer passionate about creating scalable and impactful AI-driven solutions. My expertise spans from designing and industrializing end-to-end MLOps pipelines to managing comprehensive data science workflows.
I thrive on turning data into actionable insights, optimizing workflows, integrating innovative AI solutions into real-world applications, and making Enterprise AI a reality.
🔹 Jr Data Scientist @ Business Gateways International Pvt. Ltd. (Nov 2024 – Present)
- Leading AI integration initiatives and designing scalable AI systems.
- Developing generative AI-driven recommendation systems and search engines.
- Streamlining ML operations through robust MLOps workflows (CI/CD, monitoring, version control).
🔹 Big Data & ML Engineer @ SAP (Oct 2022 – Mar 2023)
- Developed Azure-based Data Lake solutions (Databricks, Spark, MLflow).
- Created automated monitoring solutions for artifacts
- Developed an efficient MLops Framework with CI/CD in within Databricks for the end users
- Implemented a Feature Store, enhancing ML production efficiency by 15%.
🔹 Data Scientist Intern @ Rubixe Information Technology & Services (Mar 2020 – Jul 2020)
- Built data pipelines and anomaly detection models.
- Enhanced prediction accuracy by 20% using statistical and heuristic approaches.
- MSc in Artificial Intelligence Systems, EPITA School of Engineering, Paris, France (2021-2023)
- Bachelors in Computer Science, Ponnaiyah Ramajayam Institute of Science & Technology, India (2014-2018)
- Languages: Python, SQL
- Big Data & Cloud: Spark, Databricks, Azure, SAP Cloud Platform
- MLOps & CI/CD: MLflow, Jenkins, Airflow, JIRA
- Frameworks & Tools: FastAPI, Streamlit, Grafana, Plotly, Git , Langchain
- ML Libraries: NumPy, Pandas, Scikit-learn, TensorFlow, Keras, OpenCV, SHAP, Matplotlib, Huggingface
- AI Powered Search Engine: Deployed an efficient Hybrid Search Engine and RAG based Product Summary system .
- MLOps Framework & Feature Store: Deployed efficient ML workflows from prototyping to production.
- Continual Learning for Facial Recognition: Implemented drift detection, adaptive modeling, and Bayesian inference.
- Model Explainability Pipeline: Leveraged SHAP and MLflow for transparent ML model deployment.
Feel free to reach out for collaboration, mentoring, or just a friendly chat about AI and Data Science!
✨ Keep Exploring, Keep Learning! ✨