AI/ML Engineer focused on building production-grade ML systems, LLM applications, and intelligent automation.
- Large Language Models (LLMs), RAG pipelines, and memory-augmented agentic systems
- Multi-agent orchestration, retrieval workflows, and context-aware chatbot architecture
- ML system optimization, inference latency reduction, and model monitoring/evaluation
- Computer Vision: OCR, object detection, image preprocessing, segmentation, and post-processing pipelines
- Synthetic data generation and tabular modeling using SDV and CTGAN
- Fraud detection, biometric verification, and deepfake/catfish detection systems
- Healthcare AI workflows: OCR/PDF parsing, structured extraction, and disease prediction pipelines
- End-to-end MLOps and deployment with Docker, RunPod, API integrations, and cloud tooling
- Languages: Python, R, C++, SQL, JavaScript, TypeScript, HTML, CSS
- ML/AI: PyTorch, TensorFlow, Keras, scikit-learn, XGBoost, CatBoost, LightGBM, Hugging Face, OpenCV, Ultralytics
- Data: Pandas, NumPy, Polars, Matplotlib, Seaborn, NLTK, spaCy, SDV, CTGAN
- Backend and APIs: FastAPI, Flask, Node.js, Express.js, REST APIs, OpenAI APIs
- MLOps and Infra: Docker, RunPod, Git/GitHub, GitHub Actions, Render, Streamlit, Gradio
- Databases and Analytics: PostgreSQL, MySQL, Redis, Power BI, Kaggle