My Work

Intern-MLE, Khiladi

Technical Support Engineer, Motive

Portfolio

• Enhanced multi-label classification models using XGBoost and Random Forest, achieving a 25% improvement in prediction accuracy for bowling error detection through rigorous 10-fold cross-validation.

• Implemented a containerized FastAPI-based API deployment using Python to reduce inference latency by 30% and enable real-time predictions across multiple client applications.

• Streamlined the ML model deployment pipeline by establishing CI/CD workflows on Google Cloud Build, decreasing deployment time by 40% and enhancing production scalability.

• Analyzed performance data using Grafana and DataDog, identifying critical trends and resolving 85% of recurring issues to enhance user experience and reduce support ticket volume by 40%.

• Coordinated with cross-functional teams to alert stakeholders on emerging product health trends, leading to proactive adjustments that improved system reliability by 25% and boosted customer satisfaction

My Projects

RL-Based Collision Avoidance for UAVs

• Developed and implemented a Reinforcement Learning (RL) framework that enhanced the navigation capabilities of UAVs in earthquake-stricken environments, achieving a 40% reduction in collision rates during simulated missions within a complex terrain.

• Created an advanced Unity 3D simulation featuring over 50 static and dynamic obstacles, which enabled real-time testing of UAV navigational strategies; this simulation contributed to the successful training of an RL agent capable of adapting to diverse environmental challenges with a 85% success rate.

Grand Prix 2025 Prediction Model

• Developed a prediction model in Python, incorporating 4+ key features derived from historical data using Pandas and NumPy, resulting in a 74% accuracy in identifying race outcomes.

• Employed Scikit-Learn's regression capabilities to build and assess multiple predictive models, systematically tuning hyperparameters via GridSearchCV to achieve a high accuracy in forecasting the Grand Prix 2025 winner.

• Generated 3+ data visualizations using Matplotlib and Seaborn for internal analysis, effectively communicating key patterns in the F1 dataset and the confidence levels of the winner predictions.

Cost-Effective and Precise SMDs for PCBs

• Engineered a cost-efficient Pick-and-Place machine prototype utilizing machine learning principles, reducing production costs by 40% while maintaining industry-standard accuracy levels of 99.8%, enabling broader market accessibility.

• Implemented advanced automation algorithms that increased assembly throughput by 35%, decreasing component placement time from an average of 12 seconds to under 8 seconds per device, significantly boosting manufacturing efficiency.

• Developed a scalable design framework for SMD placement on PCBs, enhancing precision in component alignment from ±0.05mm to ±0.01mm, which contributed to a 20% reduction in defect rates during initial testing phases.

About Me

I am a dedicated AI/ML Engineer with a robust foundation that spans from chip design to advanced intelligent systems. My journey in the tech world began with a deep dive into FPGA/ASIC design, which provided me with an unparalleled understanding of hardware architectures. This unique background now enables me to approach AI challenges with a comprehensive perspective, ensuring solutions are not just innovative but also efficient and optimized at every level.

A significant achievement in my academic career involved crafting a Reinforcement Learning-based collision avoidance framework for UAVs operating in earthquake-struck areas. This Master's thesis project wasn't just about implementing cutting-edge AI; it was about developing resilient, autonomous systems capable of navigating highly complex and unpredictable environments.

Beyond my core development, I am deeply committed to bringing advanced AI from research to practical, production-ready deployment. My focus is firmly on operationalizing machine learning models and creating scalable, reliable MLOps pipelines. I thrive on transforming complex AI concepts into actionable systems that deliver tangible value and drive innovation in real-world scenarios.

Tools and Technologies

Python

Git

GCP

FastAPI

Scikit-learn

SQL

Tensorflow

PyTorch

VS Code

Docker

Let's talk.

Feel free to reach out to me for job opportunities, collaborations, or just to say hello!