AI/ML Engineer specializing in building intelligent and scalable AI systems using machine learning, deep learning, and Large Language Models (LLMs). Experienced in developing end-to-end AI applications including LLM development and fine-tuning, GPT, Claude, and LLaMA model customization, prompt engineering, prompt optimization, Retrieval-Augmented Generation (RAG) systems, and large-scale LLM deployment and optimization.

Strong expertise in Natural Language Processing (NLP) including text classification, sentiment analysis, Named Entity Recognition (NER), machine translation, conversational AI, and chatbot development. Skilled in Computer Vision tasks such as object detection, object recognition, image segmentation, image classification, video analysis, object tracking, and edge AI deployment optimization.

Experienced in deep learning architecture design, including CNNs, RNNs, and Transformer-based models, with strong knowledge of transfer learning, model fine-tuning, neural network optimization, model compression, and custom deep learning architecture development. Additionally experienced in Generative AI technologies such as GANs, Variational Autoencoders (VAEs), diffusion models, text-to-image generation, image-to-image generation, AI art applications, and Stable Diffusion customization.

Hands-on experience in Reinforcement Learning, including policy optimization, multi-agent reinforcement learning systems, robotics control algorithms, and game AI development. Also experienced in Time Series and forecasting systems, including financial modeling, stock prediction, demand forecasting, anomaly detection, and sequential data analysis.

Skilled in MLOps and AI system deployment, including CI/CD pipelines for machine learning models, model monitoring, model retraining, experiment tracking, and scalable infrastructure optimization using cloud platforms. Experienced with containerization and orchestration technologies such as Docker and Kubernetes, and experiment tracking tools including MLflow and Weights & Biases.

Proficient in modern AI/ML frameworks and libraries, including PyTorch for deep learning research and production systems, TensorFlow and Keras for enterprise AI deployment, Hugging Face Transformers for LLM and NLP model development, OpenCV for computer vision applications, scikit-learn for classical machine learning algorithms, and JAX for high-performance machine learning research.

Experienced with cloud-based AI infrastructure including AWS SageMaker, Google Cloud AI Platform with TPU acceleration and AutoML capabilities, and Azure Machine Learning for enterprise-grade AI solutions.