- 🎓 I’m currently pursuing my Master’s in Computational Data Science at University of Florida
- 💼 3 years of professional experience in GenAI, Classical Machine Learning, Time Series Forecasting, MLOps, and Computer Vision
- 🔭 Worked on projects in Energy, Legal AI, Finance, and Computer Vision domains
- 🌱 Actively exploring LLM Fine-tuning, RAG Applications, and Advanced Deep Learning
- 💬 Ask me about Python, Scikit-learn, TensorFlow, PyTorch, Keras,MLOps (AWS, Azure, GCP)
- 📫 Reach me at [email protected]
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Cloud Platforms:
AWS (Bedrock, App Runner, Elastic Beanstalk, Lambda, S3, EC2) • Azure (AKS, VMs, Blob Storage, Key Vault, Cosmos DB, OpenAI) • GCP (basic exposure) -
Big Data & Processing:
Databricks • PySpark • Greenplum • Space Time Insight (STI) • EAS Analytics Warehouse -
Machine Learning & Deep Learning:
Scikit-learn • XGBoost • TensorFlow • PyTorch • LSTM • Classical ML (time series, anomaly detection, clustering, churn analysis) -
LLMs & Generative AI:
LLaMA 2 fine-tuning • OpenAI GPT (O3, GPT-4) • AWS Nova Pro • Multimodal RAG (text + images) • Hugging Face Transformers • RAG pipelines with vector DBs -
Computer Vision:
YOLOv8 (detection & segmentation) • custom vision pipelines • multimodal embeddings -
Data Engineering & MLOps:
FastAPI (model APIs) • MLOps on AWS & Azure • pipelines for data collection → cleaning → training → deployment -
Databases & Storage:
SQL • Azure Cosmos DB • Vector Databases (Pinecone, FAISS, Chroma) • Oracle MDMS -
Visualization & BI:
Power BI • STI dashboards • Matplotlib • Plotly -
Other Skills:
Speech-to-text (open-source models) • Recommendation Systems • Anomaly Detection • Transformer load forecasting • RAG evaluation metrics
- ⚡ RAG Pipelines: Built for finance, manufacturing, and pharma use cases (Azure OpenAI, AWS Bedrock, Hugging Face).
- 🧠 LLM Fine-tuning: Trained LLaMA 2 (7B) on domain-specific tasks and hosted locally.
- 👁️ Computer Vision (Cobra Vision): Car damage detection and cost estimation using YOLOv8 + GPT on Azure AKS.
- 📈 Churn & Segmentation: Built churn KPIs, clustering, and engagement insights for member datasets.
- 🔌 Energy Analytics: Transformer load forecasting and anomaly detection using large-scale utility data.