Amna M.

A production integration and troubleshooting engineer who can cross the boundary between AI, Python backends, databases, documents, automation, and engineering systems. I design and build reliable AI, LLM, RAG, NLP, machine learning, deep learning, and automation systems where retrieval quality, execution logic, and workflow stability matter. What I Build: ✅ RAG Systems: hybrid semantic + BM25 retrieval, reranking, vector databases, evaluation pipelines ✅ LLM Agents: LangChain, LangGraph, LlamaIndex, multi-step AI agents, tool calling ✅ NLP Pipelines: text classification, summarization, sentiment analysis, embeddings, POS tagging ✅ Language Models: RNN, LSTM, Transformers, BLEU/ROUGE evaluation ✅ ML/DL Systems: classification, regression, clustering, forecasting, CNNs, GANs, XGBoost ✅ Computer Vision: image processing, feature extraction, object detection, YOLO, transfer learning ✅ Robotics & Autonomous Systems: ROS, robot perception, localization, navigation, sensor fusion ✅ Graph AI: Graph Neural Networks, knowledge graphs, graph embeddings, graphical models ✅ Probabilistic AI: Bayesian networks, stochastic systems, Markov models, variational inference ✅ HCI/BCI & Signal Processing: EEG preprocessing, FFT, filtering, feature extraction, ML classification ✅ AI Automation: n8n, Make, OpenAI, Claude, Grok, Ollama, API integrations ✅ AI Lead Generation: scraping, enrichment, data cleaning, LLM-powered research automation ✅ Research & Prototyping: LaTeX, Overleaf, literature review, academic writing, journal research Core Skills: Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, Large Language Models, Generative AI, RAG, Vector Search, AI Agents, Prompt Engineering, Retrieval Evaluation, Data Preprocessing, Feature Engineering, Model Training, Hyperparameter Tuning, Transfer Learning, Reinforcement Learning, Explainable AI, Computer Vision, Robotics, ROS, Graph Neural Networks, Knowledge Graphs, Probabilistic Models, Stochastic Systems, Applied Linear Algebra, Optimization, Signal Processing, EEG Analysis, Human-Computer Interaction, Research Methodology. Technical Stack: AI & LLMs: OpenAI API, Claude API, Groq, Ollama, Hugging Face, LangChain, LangGraph, LlamaIndex RAG & Vector Search: FAISS, Pinecone, ChromaDB, Vector Embeddings, BM25, Semantic Search, Reranking Programming & Backend: Python, Django, FastAPI, REST APIs, Webhooks, JavaScript, TypeScript Automation & Integration: n8n, Make, Appium, Selenium, UIAutomator2 Databases: PostgreSQL, MongoDB, ArangoDB, SQL Machine Learning & Data: PyTorch, TensorFlow, Keras, Scikit-learn, Pandas, NumPy, OpenCV, YOLO Frontend & Visualization: React, Next.js, Streamlit, Plotly Development & Deployment: Git, GitHub, Docker, Linux, Jupyter Notebook, Google Colab I focus on practical AI systems that are testable, maintainable, and reliable after delivery. I clarify requirements early, check data quality, define evaluation criteria, and build workflows with validation, visibility, and error handling. If you need an AI prototype, RAG chatbot, NLP model, ML pipeline, research implementation, or automation workflow, send me your use case, and I’ll suggest a clear approach.