Designing and deploying production-grade LLM applications, LangGraph multi-agent systems, and intelligent RAG pipelines.

I am an Applied AI & Generative AI Engineer specializing in translating complex business problems into reliable, production-ready AI systems. My experience spans building stateful Agentic AI workflows, designing advanced Retrieval-Augmented Generation (RAG) systems, and deploying Machine Learning algorithms and Deep Learning architectures.

I focus on combining state-of-the-art Transformers and sequence models (RNNs/CNNs) with robust Python-based backends to build explainable, fast, and secure AI systems.

• Agentic AI & Orchestration: Stateful Multi-Agent architectures (LangGraph), autonomous loop design, self-correction algorithms, and tool calling/API integration.
• Advanced RAG Pipelines: Dense/Sparse hybrid retrieval, vector search/embeddings, semantic content extraction, document pre-processing, and semantic caching.
• Deep Learning & Model Architectures: Transformers (fine-tuning, attention layers), Convolutional Neural Networks (CSSs) for visual analysis, and Recurrent Neural Networks (RNNs) for sequential forecasting.
• Machine Learning Algorithms: Supervised & Unsupervised learning, classification, clustering, regression, TF-IDF vectorization, Cosine Similarity, and statistical model evaluation.
• Python AI Ecosystem: Asynchronous FastAPI backends, structured schemas (Pydantic), data manipulation (Pandas, NumPy), and modeling (Scikit-learn).

Rishabh Software Pvt. Ltd. | Vadodara, India
Jan 2026 – Jun 2026

• Developed stateful AI Agents, multi-agent orchestration workflows, and high-performance async FastAPI backends.
• Integrated Azure AI Services and worked with structured data validation and MLOps deployment architectures.