Building intelligent systems. Engineering the future of AI.
Specializing in deep learning, computer vision, and NLP. I transform complex mathematical models into production-ready solutions. Focused on performance, scalability, and rigorous empirical validation.
Designing scalable neural networks and robust data pipelines.
Real-time object detection and image segmentation models.
Advanced language modeling and sentiment analysis engines.
[Accuracy: 98.4% · ResNet-50]
[Latency: 12ms · Inference]
AI & ML Engineering Stack
Technical proficiencies in building scalable AI models, data pipelines, and automated engineering solutions.
Python
Production-grade scripting, data pipelines, and model deployment workflows.
Machine Learning
Supervised learning, neural networks, and predictive model optimization.
Deep Learning
Architecting CNNs, RNNs, and Transformers for complex data patterns.
SQL
Complex queries, database design, and efficient data retrieval strategies.
Data Processing
Vectorized operations, data cleaning, and large-scale feature engineering.
Visualization
Statistical plotting, model performance metrics, and data storytelling.
NLP
Text processing, sentiment analysis, and language model fine-tuning.
Computer Vision
Image classification, object detection, and feature extraction pipelines.
DevOps Tools
Containerization, version control, and automated deployment workflows.
Review technical project implementations?
Explore my AI models, data analysis, and engineering repositories.
AI Engineering Projects
Production-grade machine learning models and computer vision pipelines. Each project demonstrates technical rigor, optimized inference, and scalable architecture.
Mean Average Precision
94.2% mAP
Inference Latency
12ms
Model Architecture
YOLOv8-Small
Real-time Object Detection
Autonomous Navigation Module
High-speed object detection pipeline optimized for edge deployment, utilizing custom-trained neural networks for real-time environmental perception.
Engineering Highlights:
- Custom dataset annotation and augmentation for robust feature extraction.
- Model quantization and pruning to achieve sub-15ms inference latency.
- Integration with ROS2 for seamless autonomous navigation feedback loops.
Tech Stack:
Classification Accuracy
89.5%
Data Throughput
5k req/s
Model Type
Transformer-BERT
Financial Sentiment Engine
Market Trend Analysis
Scalable NLP pipeline designed to process financial news feeds and extract sentiment vectors for predictive market trend analysis.
Engineering Highlights:
- Fine-tuned BERT transformer models on domain-specific financial corpora.
- Distributed data ingestion pipeline using Kafka for real-time processing.
- Vector database integration for efficient semantic search and retrieval.
Tech Stack:
AI Engineering Milestones
Chronological overview of practical internships, academic achievements, and industry certifications.
Advanced Neural Architecture Research
Developing scalable ML pipelines using PyTorch and TensorFlow. Optimizing inference latency for real-time computer vision and NLP applications in production environments.
Applied AI Lab & Data Science Intern
Implemented custom transformer architectures and CNNs for image classification. Achieved 94% accuracy on benchmark datasets through rigorous hyperparameter tuning.
Academic Research & Data Analysis
Mastered core statistical modeling, feature engineering, and algorithmic logic. Built predictive models for time-series forecasting and classification tasks.
Technical Foundations & Early Research
Deep dive into mathematical foundations of AI, linear algebra, and probability. Initiated first neural network experiments and data preprocessing workflows.
Ready to build the future?
Discuss ML engineering opportunities, research projects, or technical consulting.
Let’s discuss your AI/ML needs
I am available for engineering roles and technical collaborations. Reach out to review my full resume or discuss potential projects.