I help businesses turn AI, automation, and data into measurable outcomes — from predictive models to production-ready agentic systems.
I am a results-driven technology professional with over a decade of experience at the intersection of Artificial Intelligence (AI), Machine Learning (ML), Robotic Process Automation (RPA), and Digital Transformation. My core expertise lies in designing and deploying end-to-end AI solutions, spanning Computer Vision, Audio Analytics, Natural Language Processing (NLP), and advanced predictive modeling.
I specialize in taking ideas from concept to production — ensuring that models are not only accurate in theory but also deliver measurable business value when integrated into live systems. From building scalable AI pipelines and automating enterprise workflows to implementing intelligent dashboards and conversational bots, I focus on solutions that optimize efficiency, reduce costs, and unlock actionable insights.
Beyond project delivery, I am passionate about knowledge sharing and community engagement — mentoring peers, writing about evolving trends in AI and automation, and contributing to open-source projects that make cutting-edge technology accessible to a wider audience.
Python, TensorFlow/Keras, PyTorch, predictive modeling
Image classification, object detection, audio sentiment & analytics
OpenAI API, LangChain, RAG, embeddings, semantic search
Workflow automation, API integration, chatbots
Power BI, Tableau, Metabase, Superset
Azure, AWS basics, Git, MLOps fundamentals
Churn model, customer classification for loans and transaction volumes, and IVR call type prediction to reduce wait times.
Image classification for field visits and object detection for self and competitor logos.
Dead air detection and audio sentiment analysis for customer support optimization.
Email sentiment analysis, OCR-based document detection, and text extraction.
Organization-wide reporting bot with role-based access control for scheduled and ad-hoc insights.
Learn how to implement Retrieval-Augmented Generation (RAG) using Amazon Bedrock with Python, embeddings, and vector databases for enterprise-grade AI search.
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Learn how tokenization and vectorization transform text into numerical representations for deep learning models. Includes Python examples with Keras, Word2Vec, and BERT.
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Learn how to use Vanna.AI with Python to convert natural language questions into SQL queries and run them against your database. Step-by-step guide with code examples, setup, and use cases.
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