🎓 Machine Learning Engineer | Data Scientist | Generative AI Enthusiast
📍 Based in the U.S. | 🌐 Open to opportunities in ML and Data Science
📫 [email protected] | LinkedIn | GitHub
Machine Learning Engineer and Data Scientist with 3+ years of industry experience building scalable ML systems, real-time predictive models, and end-to-end AI pipelines across fintech, healthcare, and biometrics domains. Recent grad with a Master’s in Information Science (Machine Learning) at the University of Arizona (May 2025), with a strong foundation in deep learning, LLM fine-tuning, cloud-native deployment, and data engineering at scale.
Proven track record of delivering production-ready solutions—ranging from financial market predictors to real-time biometric security systems—leveraging advanced ML techniques. Adept at working with large, multimodal datasets (structured, time-series, unstructured), and experienced in building and deploying LLM-based agents, reinforcement learning models, and Fine-tuned LLms to production.
Skilled in Python, SQL, PySpark, and major ML libraries (TensorFlow, PyTorch, HuggingFace Transformers). Hands-on with cloud platforms (AWS, Azure), MLOps tools (Docker, MLflow, Kubernetes), and data infrastructure technologies (Airflow, Kafka, Redshift). Passionate about research, having led academic work on retentive networks and progressive expansion for hyperspectral image classification.
Driven by a balance of technical depth and business impact, I bring a research-backed, solution-oriented mindset to every ML initiative—bridging the gap between data, insights, and real-world outcomes.
Leveraged RAG, LangGraph, Qdrant and Redis to build a multi agent system. Analyzes Resume and JD to give matching score not just based on skills from skills section but extracting skill from other section by leveraging LLM and promp-engineering.
🔧 Reinforcement learning with self-rewarding dynamics using TimesNet
📈 Achieved 40% ROI on DJI index over 3 years
🔍 Fine-tuned TinyLLaMA with QLoRA for enterprise documents 📚 Evaluated its performance on base model with deepeval
🔍 Fine-tuned TinyLLaMA with LoRA + RAG for financial document QA
📚 Indexed 100K+ filings with ChromaDB and served with FastAPI
- 💻 GitHub
- 📬 Email: [email protected]
"Build things that matter, and optimize what learns from them."