Ajay P.

I am an AI/ML Engineer specializing in Generative AI, Large Language Models (LLMs), AI Agents, Retrieval-Augmented Generation (RAG), and intelligent automation. My work focuses on designing and deploying production-ready AI solutions that combine state-of-the-art language models with scalable backend systems to solve real-world business problems. My expertise spans the complete AI development lifecycle, from data collection and preprocessing to model training, fine-tuning, evaluation, deployment, and monitoring. I have hands-on experience building AI-powered applications using OpenAI, Anthropic, Google Gemini, Llama, Qwen, Mistral, DeepSeek, Gemma, Phi, and other modern foundation models. I build custom AI assistants, enterprise chatbots, multi-agent systems, and RAG pipelines using frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, and AutoGen. I work extensively with vector databases including Pinecone, ChromaDB, FAISS, Weaviate, Qdrant, and Milvus, implementing semantic search, hybrid search, reranking, and knowledge retrieval systems. My NLP expertise includes text classification, sentiment analysis, named entity recognition, summarization, question answering, topic modeling, information extraction, semantic similarity, document intelligence, and multilingual language understanding. I also have experience developing multimodal AI applications that combine text, images, and structured data. I am experienced in fine-tuning open-source LLMs using LoRA, QLoRA, PEFT, Hugging Face Transformers, TRL, and distributed training techniques. My work includes instruction tuning, domain adaptation, synthetic data generation, evaluation pipelines, and optimization for inference efficiency. Beyond LLMs, I have strong experience in classical machine learning, deep learning, computer vision, recommendation systems, time series forecasting, and predictive analytics using frameworks such as PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM, and CatBoost. I also design scalable AI infrastructure and APIs using FastAPI, Docker, Kubernetes, Redis, PostgreSQL, MongoDB, Celery, WebSockets, and cloud platforms including AWS, Azure, and Google Cloud. My focus is on building reliable, secure, and production-ready AI systems that integrate seamlessly with existing business workflows. My recent work includes: * AI Agents capable of autonomous reasoning and tool usage. * Enterprise RAG systems with advanced retrieval, reranking, and citation support. * LLM-powered document processing and knowledge management platforms. * Recommendation engines and personalized AI applications. * Real-time AI systems with streaming inference and WebSocket communication. * Fine-tuning and deployment of open-source LLMs for domain-specific use cases. * End-to-end MLOps pipelines for model training, deployment, monitoring, and continuous improvement. I am also proficient in data engineering tasks including web scraping, data annotation, ETL pipelines, feature engineering, and working with large-scale structured and unstructured datasets. My research interests include LLM reasoning, multimodal AI, AI Agents, Retrieval-Augmented Generation, reinforcement learning, efficient model fine-tuning, and trustworthy AI. I continuously explore the latest advancements in Generative AI to deliver practical, scalable, and impactful AI solutions. Whether you need an intelligent AI assistant, an enterprise chatbot, a production-ready RAG system, an autonomous AI agent, custom model fine-tuning, or an end-to-end AI application, I can help transform your ideas into robust, high-performing solutions.