AI/ML Engineer & MLOps Specialist
Building intelligent systems with Machine Learning, automating workflows with MLOps, and creating production-ready AI solutions. Passionate about bringing AI from research to real-world impact.
My journey in the world of Artificial Intelligence and Engineering.
I'm an AI/ML Engineer specializing in building end-to-end machine learning solutions and deploying them at scale. My expertise spans from data preprocessing and model development to MLOps pipeline automation and production deployment.
With experience as an Automation Engineer and Risk Analyst, I bridge the gap between complex algorithms and practical business solutions. I'm passionate about leveraging cutting-edge AI technologies to solve real-world problems efficiently.
My professional career path.
InCred Financial Services
Owned policy development and deployment in the Business Rule Engine (BRE) production system, ensuring compliance and seamless integration with business processes. Designed and built a custom Python package called "Simulator" to verify implemented policies, reducing policy verification time by 30% and testing code complexity by 50%. Implemented CI/CD workflows with GitHub Actions to automate builds, run unit/integration tests for Simulator.
Skills: Python, GitHub Actions (CI/CD), Databricks, Metabase, SQL, Git, Excel
Fox Solutions Pvt. Ltd.
Started as an intern, developing and deploying automation pipelines focused on reproducibility, monitoring, and reducing manual intervention. Later transitioned to a full-time role, where I applied principles of version control, reliability engineering, and system monitoring to ensure scalable and consistent automation workflows across projects.
Skills: Pipeline Automation, Monitoring Systems, Version Control, PLC/SCADA Tools
A selection of my recent work in AI and MLOps.
End-to-end system predicting insurance charges. Optimized SVR model deployed on AWS with Docker. Integrated MLflow, CI/CD, and Kubeflow for full lifecycle management.
Predicting Remaining Useful Life of jet engines using NASA CMAPSS data. Hybrid CNN + LSTM model with automated MLOps pipeline for data processing and training.
Sharing knowledge through technical articles.
Explained bagging with random forest and boosting with adaboost, gradient boosting, and xgboost.
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How I built an AI bot using Retrieval-Augmented Generation (RAG) to explain my projects on GitHub and Medium articles, making it easier for HRs to understand my work without reading through code.
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A comprehensive guide to understanding location estimates and variability using box plots, covering concepts like mean, median, mode, quartiles, interquartile range, and outliers.
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![The Essential Yet
Overlooked Concept in Machine Learning: [ The Matrix ]](static/articles_images/matrix_article.png)
The Essential Yet Overlooked Concept in Machine Learning: [ The Matrix ].
Read MoreExploring data and sharing insights with the community.
Built a model using unsupervised learning to categorize fast food items by nutritional values, and then, by applying healthy thresholds for each group, ranked the fast food categories from worst to best.
Trained a Naive Bayes model for stress identification from input text, which helps an emergency messaging system alert others if stress is detected in a user’s message
Trained a Random Forest classifier using the described spot as input features.
Have a project or opportunity? Let's connect.