About
I’m a Machine Learning Engineer with 2+ years of experience building and deploying end-to-end AI solutions, including data preprocessing, feature engineering, model training, and production deployment. I have strong expertise in Python, Scikit-learn, TensorFlow, and PyTorch, along with hands-on experience in NLP and Generative AI (LLMs, RAG pipelines). I have developed scalable AI applications and deployed them using FastAPI and cloud environments. I’m particularly interested in opportunities where I can apply my skills to build real-world, scalable AI systems and contribute to impactful, data-driven solutions.
Experience3
Machine Learning Engineer
September 2025 — PresentSundar Infographic Analytics, @SNC
Developed and deployed ML/DL models using TensorFlow, PyTorch, and ONNX. Performed benchmarking and evaluation of LLMs for automation tasks. Implemented lightweight deployment pipelines using Python APIs and model optimization. Created reproducible ML experiments following MLOps practices (versioning, experiments tracking). Collaborated with engineering teams to integrate ML-based automation into production workflows.
Machine Learning Data Associate
March 2024 — September 2025SEEWISE.AI
Supported computer vision pipeline inside Django for transportation monitoring automation. Conducted data annotation, preprocessing, feature extraction, and model evaluation.
Data Coordinator
September 2022 — November 2023Pixstone images pvt ltd
Created Power BI dashboards and visual reports for internal teams. Processed, cleaned, and structured large datasets for analytics and reporting. Improved BI workflows by collaborating with cross-functional stakeholders.
Projects2
Leaf disease detection using Deep Learning
Built a deep learning model with Conditional GAN and Densenet121, achieving high accuracy in leaf disease detection for early diagnosis. Performed hyperparameter tuning and improved prediction accuracy of CV models.
- Deep Learning
- Conditional GAN
- Densenet121
- SVM
Image recognition of four rice leaf disease
Achieved 96% accuracy with CGAN and Densenet121 for early leaf disease detection, reducing crop loss.
- CGAN
- Densenet121
Education2
M.Sc., Data Science
2022SASTRA University
General
B.SC., Electronics & Computer Science
2020SASTRA University
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