Machine Learning Engineer | Data Scientist | Full-Stack ML Developer

I build end-to-end machine learning solutions—from data exploration to production deployment.

What makes me different: Most data scientists build notebooks. I build production systems.

• ✅ Complete ML pipelines (data → model → deployment)
• ✅ Production-ready code with logging and monitoring
• ✅ Cloud deployment expertise (AWS, Docker, CI/CD)
• ✅ Real-world projects with quantified results
• ✅ Full-stack thinking: not just models, but scalable systems

Currently: Building ML solutions for startups | Freelancing on Upwork | Graduating 2025

End-to-End ML Pipeline with AWS Deployment

What it does:

• Predicts student math scores with 88%+ R² accuracy
• Processes 1,000+ student records with 8 features
• Compares 7 different ML algorithms (Random Forest, XGBoost, Gradient Boosting, etc.)
• Hyperparameter optimization using RandomizedSearchCV
• Flask web app for real-time predictions
• Deployed on AWS Elastic Beanstalk for production use

Technologies: Python, Pandas, NumPy, Scikit-learn, XGBoost, Flask, AWS, Docker

Key Achievement: Complete pipeline from raw data to production—not just a notebook.

Production ML Deployment Automation

What it does:

• Automates ML model deployment using GitHub Actions
• Reduces deployment time: 30+ minutes (manual) → 5 minutes (automated)
• Eliminates deployment errors through automated testing
• Docker containerization + AWS ECR + EC2 + GitHub Actions
• Complete Infrastructure-as-Code in Git version control
• Zero-downtime deployments with health checks

Technologies: Docker, AWS (ECR, EC2), GitHub Actions, Python, YAML, CI/CD

Key Achievement: Demonstrates production engineering expertise—differentiates from typical data scientists.

• Algorithms: Regression, Classification, Ensemble Methods, Time-Series Analysis
• Optimization: Hyperparameter Tuning (GridSearchCV, RandomizedSearchCV), Cross-Validation
• Model Comparison: Multi-algorithm evaluation, performance metrics, feature importance
• Libraries: Scikit-learn, XGBoost, LightGBM, Pandas, NumPy, Matplotlib, Seaborn

• Core: Python, Jupyter Notebook, Git/GitHub
• Data Processing: Pandas, NumPy, Data cleaning, Feature engineering
• Visualization: Matplotlib, Seaborn, Plotly, Power BI, Tableau
• Web Frameworks: Flask, Streamlit

• Cloud Platforms: AWS (Elastic Beanstalk, EC2, ECR), Azure
• Containerization: Docker, container orchestration
• CI/CD: GitHub Actions, automated testing, deployment automation
• Infrastructure: Infrastructure-as-Code, monitoring, scaling

• Databases: MySQL, SQL queries, joins, aggregations
• Version Control: Git, GitHub, GitHub Actions
• Other: Jupyter, VS Code, Linux command line

• LLM: Large Language Models, AI Agents, Agentic Workflows
• Advanced Techniques: Retrieval-Augmented Generation (RAG), Prompt Engineering, Tool Calling
• Emerging Skills: Multi-Agent Systems, LLM Deployment, Context Engineering

• Computer Science Student (Graduating 2025)
• Hands-on Experience: 5+ complete ML projects with production deployment
• Self-Taught: Cloud infrastructure, DevOps, production ML systems
• Certifications: Cisco Python Essentials 1, Data Science & Analytics
• Currently Learning: AI Agents, Agentic Workflows, LLM Deployment, RAG

✅ End-to-End Solutions – Data → Model → Deployment (not just analysis) ✅ Production Mindset – Code is documented, tested, and scalable ✅ Full-Stack Thinking – ML + Web App + Cloud Infrastructure ✅ Quantified Results – 88%+ accuracy, 5-minute deployments, measurable impact ✅ Quick Learner – Rapidly adapt to new tools and challenges ✅ Problem Solver – Focus on business value, not just technical metrics

• Startups needing end-to-end ML solutions
• Teams with models that need deployment help
• Companies lacking ML ops expertise
• Quick turnarounds requiring production systems in weeks
• Scalable systems requiring infrastructure thinking

• Freelance Projects on Upwork (ML, Data Science, Deployment)
• Contract Work (part-time, full-time)
• Internships (Data Science, ML Engineering)
• Collaboration (open source, side projects)

"Transforming raw data into production ML systems."

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