Mohammed Ashraf Hussain

Innovative AI Engineer (Artificial Intelligence Engineer) with a B.E. in Computer Science (AI & ML), Osmania University (2021–2025). Expert in Generative AI, Large Language Models (LLMs), foundation models — including GPT-4, BERT, LLaMA, Gemini — and advanced fine-tuning techniques (LoRA, QLoRA, RLHF, instruction tuning, parameter-efficient methods). Hands-on experience building RAG (Retrieval-Augmented Generation) pipelines with LangChain, LlamaIndex, FAISS, ChromaDB, Pinecone, and deploying AI Agents using tool use, function calling, multi-agent systems. Deep expertise in NLP (text classification, sentiment analysis, NER, summarization, question answering, embeddings, semantic search, tokenization, attention mechanism) and computer vision (OpenCV, OCR, object detection). Proficient in the full ML lifecycle: supervised learning, unsupervised learning, model training, model evaluation, hyperparameter tuning, cross-validation, model optimization, model deployment, model inference, model serving, and model monitoring. Skilled in MLOps (MLflow, DVC, Docker, Kubernetes, CI/CD, GitHub Actions), cloud AI (AWS SageMaker, GCP Vertex AI, Azure), and prompt engineering (zero-shot, few-shot, chain-of-thought). Strong problem solving, problem-solving, communication, collaboration, and agile delivery skills. Ready to build production-grade AI systems as a fresher.