Vivek J.

Want to bridge the gap between AI hype and real ROI? I design and deploy production-grade GenAI, Agentic RAG systems, and advanced ML models that cut costs and scale businesses. With 14+ years of experience across E-commerce, FinTech, HealthTech, and Supply Chain, I don't just build models—I build intellectual property (including patent-pending architecture) that drives millions in revenue. ### Core Expertise & How I Can Help You: * **Generative AI & Agents:** Building autonomous agentic frameworks (CrewAI, AutoGen) and custom RAG pipelines to automate complex enterprise workflows. * **Predictive Analytics & Forecasting:** Drastically reducing forecast errors (e.g., cutting demand forecasting WAPE from 100% to 35%). * **Marketing & Ad-Tech Intelligence:** Multi-touch attribution modeling (Nielsen standards) and Amazon campaign optimization. * **MLOps & Scalable Infra:** Deploying robust models on AWS, Azure, and Google Cloud using Docker, MLflow, and Vector Databases. ### Featured Case Studies: * **Patent-Pending Product Matching:** Designed a multi-modal architecture to identify competitor product matches with elite precision. * **Agentic RAG System:** Built a high-efficiency query system over massive, mixed structured/unstructured datasets. * **Identity Graphs at Scale:** Handled billions of nodes to reduce cluster sizes and uncover hidden identity relationships. --- I also hold a degree from one of India's premier technology institutes. My dedication to integrity and excellence has been recognized through multiple awards, including being named among the top 20 emerging leaders in data and analytics at the Data World Summit in India Few of the challenging projects I have worked on: Product Matching - Designed and developed end-to-end solution using a multi model architecture to identify best matching competitor product, currently in the process of filling provisional patent Assortment Intelligence System - Developing an Agentic RAG System to efficiently handle queries on a competitor dataset composed of structured and unstructured data Multi-touch Attribution problem: Helped identify the impact of online marketing on offline sales and help you tweak campaigns in real times. ROI results were comparable to industry standard-Nielsen Amazon Campaign Optimisation: Helps in the organic growth of products sold on Amazon Social Media Search Tool - Helps in the identifying the relevant posts, sentiments and themes for a given query as input Demand Forecasting: Reduced the WAPE of thousands of products and their bundles from ~100% to ~35% Pattern Identification: Built an automated system that can identify important patterns in the data Identity graph: Found important identities and reduced the cluster sizes using billions of nodes Tools & Technologies:Programming - Python, SQL; Cloud Platforms – Azure ML, AWS, Google Vertex AI; MLOps - Docker, CI/CD, MLFlow ; Big Data – Spark; Version Control - Azure Devops, Git ; Database & Vector Search -PostgreSQL, Snowflake, Milvus, FAISS AI/ML Libraries & Frameworks: Machine Learning - Scikit-learn, XGBoost, LightGBM ; Deep Learning: TensorFlow, PyTorch, Keras; Transformers & NLP - Hugging Face Transformers, SpaCy, NLTK ; LLMs -GPT, BERT, LLama, Mistral, Qwen, Phi; Agentic Frameworks – Langchain, Autogen, Crewai