Technical Skills

Programming Languages

Primary Expertise

• Python - Production ML systems, distributed computing, data engineering
• SQL - Complex query optimization, database design, data analysis
• Java - Enterprise applications, backend services
• C++ - Performance-critical systems, algorithm optimization

Additional Experience

• MATLAB - Scientific computing, signal processing
• Apache Spark - Large-scale data processing

AI/ML Frameworks & Libraries

Deep Learning Frameworks

• PyTorch - Primary framework for research and production
• TensorFlow - Model deployment and serving
• Keras - Rapid prototyping

Computer Vision

• OpenCV - Image processing and computer vision algorithms
• Torchvision - Pre-trained models and datasets
• Pillow, Scikit-Image - Image manipulation
• Albumentations - Data augmentation
• Media Pipe - Real-time ML solutions

Natural Language Processing

• Hugging Face Transformers - LLMs and pre-trained models
• NLTK, Spacy - Text processing and linguistics
• TextBlob - Sentiment analysis
• BERT, RoBERTa - State-of-the-art NLP models

ML & Data Science

• Scikit-learn - Traditional ML algorithms
• XGBoost, AdaBoost - Gradient boosting
• H2O - AutoML and distributed ML
• Pandas, NumPy - Data manipulation and numerical computing
• Matplotlib, Seaborn, Plotly - Data visualization

ML Model Architectures & Experience

Computer Vision Models

• CNNs: VGG-16, ResNet-50, Inception Net, EfficientNet
• Object Detection: YOLO, R-CNN family (Fast R-CNN, Faster R-CNN, Mask R-CNN)
• Segmentation: U-Net
• Classic Architectures: AlexNet, LeNet-5

NLP & Language Models

• Transformers: BERT, RoBERTa, BART
• Sequence Models: LSTM, Bi-LSTM, GRU, RNN

Traditional ML

• Ensemble Methods: Random Forest, Gradient Boost, XGBoost, AdaBoost
• Classic Algorithms: Decision Trees, SVMs
• Specialized: PoseNet (pose estimation), TableNet (table detection)

Cloud Platforms & Infrastructure

Cloud Services

• AWS - EC2, S3, Lambda, SageMaker
• Microsoft Azure - AI/ML services, data platforms
• Google Cloud Platform - AI/ML, compute, storage

Databases

• Relational: Oracle (11g, 12c), MySQL
• Experience with: Complex queries, performance tuning, schema design

Development Tools & Platforms

Version Control & Collaboration

• Git - Advanced workflows, branching strategies
• GitHub, GitLab - Code review, CI/CD
• SVN, Perforce - Legacy version control systems

IDEs & Development

• PyCharm, VS Code - Primary development environments
• IntelliJ - Java development
• Jupyter Notebooks - Data exploration and prototyping

DevOps & Orchestration

• Docker - Containerization
• Apache Airflow - Workflow orchestration
• Shell Scripting - Automation

Data & Analytics Tools

• Qlikview, Power BI - Business intelligence and dashboards

Operating Systems

• Linux - Ubuntu, Fedora, RHEL (5.9, 6.10)
• Unix - System administration
• Windows - Development and deployment

Specializations

Machine Learning Domains

• Deep Learning - Neural network architectures, optimization
• Computer Vision - Image classification, object detection, segmentation
• Natural Language Processing - Text classification, sentiment analysis, language models
• Multi-Modal AI - Vision-language models, cross-modal learning

Engineering Practices

• Production ML - Model deployment, monitoring, versioning
• Distributed Systems - Scalable architecture, parallel processing
• Data Engineering - ETL pipelines, data quality, large-scale processing
• Agile Development - Scrum, sprint planning, cross-functional collaboration

Professional Certifications

• Deep Learning Specialization - Coursera (deeplearning.ai)
  • Neural Networks and Deep Learning
  • Improving Deep Neural Networks
  • Structuring Machine Learning Projects
  • Convolutional Neural Networks
  • Sequence Models
• Azure AI Fundamentals - Microsoft
  • AI workloads and considerations
  • Machine learning on Azure
  • Computer vision workloads
  • NLP workloads
• Azure Data Fundamentals - Microsoft
  • Core data concepts
  • Relational data on Azure
  • Non-relational data on Azure
  • Analytics workloads
• Google Cloud AI/ML - Google Cloud
  • Machine Learning on GCP
  • AI Platform fundamentals

This skills portfolio represents tools and technologies I’ve applied in production environments, academic research, and competitive ML challenges. I continuously expand my expertise to stay at the forefront of AI/ML engineering.