10+ years delivering production-grade GenAI, Agentic AI & MLOps systems at enterprise scale across Retail, Banking & Healthcare — turning petabyte-scale data into measurable business ROI.
I'm a Senior GenAI / AI-ML Engineer with deep expertise in Agentic AI, RAG architecture, and MLOps across multi-cloud platforms (AWS, Azure, GCP). I transform high-volume unstructured datasets into measurable business outcomes for Fortune 500 enterprises.
My work spans Retail (Walmart), Banking (5th Third Bank), and Healthcare (L.A. Care) — designing systems that process hundreds of millions of transactions, reduce costs by tens of millions, and operate at 99.9% reliability.
I specialize in responsible AI governance, NIST AI RMF compliance, and building explainable ML systems that satisfy regulated-industry auditors while delivering real-world performance.
10+ years building production AI/ML systems at enterprise scale across multiple industries and cloud platforms.
Leading the design and deployment of edge-optimized Agentic GenAI platforms and scalable multi-cloud Data Lakehouses for one of the world's largest retailers — impacting 4,000+ global locations.
Operationalized fraud prediction and risk platforms in a highly regulated banking environment, integrating GraphRAG, LLMOps pipelines, and NIST AI RMF governance.
Built HIPAA-compliant clinical decision support systems and ML pipelines automating medical reviews for 1.2M+ active healthcare members.
Built automated vehicle valuation and ANN search systems for auction platforms processing 5TB+ of historical vehicle data.
Built foundational Big Data ingestion pipelines and Hadoop-based data warehouses for regional sales analytics and marketing intelligence.
A full-stack AI/ML engineer's toolkit spanning GenAI, MLOps, cloud platforms, big data, and responsible AI governance.
Highlights from enterprise AI/ML systems I've designed and delivered, with measurable real-world impact.
Enterprise Agentic GenAI system on Amazon Bedrock + LangGraph with autonomous agents performing real-time inventory risk analysis, demand forecasting, and vendor risk assessment across 4,000+ global Walmart locations.
XGBoost + Vertex AI fraud prediction system processing 8 million daily banking transactions with real-time anomaly detection, SHAP explainability, and SR 11-7/NIST AI RMF governance at 5th Third Bank.
Scikit-learn + XGBoost clinical AI platform automating medical necessity reviews for 1.2M+ active healthcare members with NLP/OCR pipelines, FHIR R4 integration, and full HIPAA + CCPA compliance at L.A. Care.
XGBoost + LightGBM ensemble vehicle valuation system with FAISS ANN search supporting 2M+ monthly auction users. BERT-based sentiment analysis on dealer feedback, processing 5TB+ historical vehicle inventory data at Cox Automotive.
Enterprise-grade Data Lakehouse using Snowflake + Amazon Redshift + Databricks, integrating real-time event-driven Kafka streams, 15TB+ data lake, and autonomous inventory procurement across multi-cloud AWS + GCP + Azure environments.
Feedback from those I've worked with on production AI/ML systems across enterprise environments.
Manish's ability to architect Agentic AI systems at Walmart was extraordinary. He didn't just build the LangGraph platform — he thought through the entire operational cost model and delivered a 30% reduction. His understanding of both the ML stack and business impact is genuinely rare in this field.
Working with Manish on the fraud detection platform at 5th Third Bank was a masterclass in regulated AI. He navigated SR 11-7, NIST AI RMF, and SOX requirements without slowing down delivery, and the SHAP explainability framework he built became the gold standard for our audit processes.
Open to senior GenAI/ML engineering roles, consulting engagements, and enterprise AI architecture discussions.
