Shivprasad Navnath Dhule
I’m Shivprasad Dhule, an AI Engineer with 3+ years of experience building real-world Machine Learning, Generative AI, and Agentic AI systems for finance and enterprise clients.
About
I’m Shivprasad Dhule, an AI Engineer with 3+ years of experience building real-world Machine Learning, Generative AI, and Agentic AI systems for finance and enterprise clients. I’ve worked on compliance and regulation-focused solutions that help teams automatically track rule updates, detect anomalies, and prioritize what needs attention first—bringing AI directly into everyday business workflows.
Most recently, I’ve delivered production agentic systems in fast-moving Agile teams, including a multi-agent solution at Michelin that helps sales teams choose the right agricultural tires using structured vehicle databases and technical manuals. I also lead end-to-end MLOps for secure, scalable deployments (RAG pipelines, evaluation, GPU-based serving, and API backends), with strong hands-on experience across Azure and AWS to keep data private and systems reliable.
- 3 years' experience
- Joined October 2026
Industry experience
- Financial Services
- Professional Services
- Software & Internet
- Government
- Education
- Computers & Electronics
Work experience· 10 roles
Joined Michelin’s international Data Science team after a technical interview and delivered AI capabilities in a fast-moving global Agile environment. Delivered embedded AI workflows for sales enablement, combining enterprise data, technical documentation, and retrieval/evaluation pipelines. Worked through end-to-end delivery from prototyping to UAT readiness, collaborating with platform and IT stakeholders and operating within secure Azure setups.
Embedded within Michelin’s international data science team, delivering AI solutions in a fast-moving global Agile environment. Delivered a multi-agent assistant to support tire selection decisions using structured vehicle data and technical tire manuals/specs. Built an automated document understanding and evaluation pipeline to extract and assess answers from a lengthy technical manual with tables and images.
Built production-grade generative and multi-agent AI solutions for business users. Developed a LangGraph multi-agent system that helps Michelin sales teams select the right agricultural tires by connecting a 33,000+ vehicle database with tire specification files and technical manuals. Implemented document ingestion and evaluation using LlamaParse for complex manuals (tables and images), including an automated test system with sample questions and scoring. Partnered with platform/DevOps/IT teams to test and launch via UAT, and managed Azure cloud setup using Managed Identity, Key Vault, and Azure ML.
Education
Certifications and awards
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