Tools and Technologies | Data & ML Libraries: pandas, numpy, scipy, scikit-learn, tensorflow, keras, OpenCV, fuzzywuzzy, transformers, Scrapy LLM & NLP: Hugging Face (PEFT, TRL, Trainer), LangChain, LangGraph, Ollama, OpenAI API (GPT models, Batch API), NER, POS, Semantic Similarity, Text Mining, LDA, Sequence-to-Sequence with Attention MLOps & Deployment: Docker, FastAPI, Streamlit, Celery, Flask, AWS (S3, EC2, Lambda, ECR, SageMaker, API Gateway, DocumentDB), GitHub, Logging Data Engineering: ETL pipelines, Batch APIs, Time Series Forecasting, Statistical Analysis, Data Management, Data Manipulation Dev Tools: Jupyter Notebook, PyCharm, VScode, Jira, Confluence
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Other | Machine Learning: Supervised (KNN, Linear/Logistic Regression, SVM, Decision Trees, Random Forest, XGBoost, Neural Networks) Unsupervised (K-means, DBSCAN, Agglomerative Clustering, PCA)
Deep Learning: CNN, RNN, Transformers, Transfer Learning, Fine-tuning (including LoRA on small consumer GPUs) NLP & LLM Engineering: Prompt Engineering, Fine-tuning LLaMA models with LoRA, Custom LangChain agents, Training LLMs using low-rank adaptation principles Computer Vision: Image Classification, Object Detection, Image Recognition, OCR
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