AI/ML and Strategy Professional | Enterprise Transformation & Agentic Systems
14+ years scaling enterprise systems, optimizing complex operations, and driving commercial impact. Now building production-grade agentic AI systems that automate decision-making and unlock organizational potential.
I bridge strategy and engineering — translating business challenges into scalable AI solutions. Expertise in LangGraph multi-agent systems, RAG architectures, generative AI workflows, and enterprise deployment.
Now
Building
Shaping AI native Business workflows of the future
Reading
The Innovator's Dilemma by Clayton Christensen.
Expertise
LangGraph, multi-agent systems, RAG pipelines, generative workflows, LLM orchestration
Production ML systems, FastAPI, Python, Docker, AWS SageMaker, Databricks, scalable architectures
Enterprise transformation, P&L management, AI use-case discovery, business analytics, commercial impact
$20M+ revenue expansion • 85% cycle time reduction • $14M EBITDA uplift • $1M+ margin capture
Recent Focus
Stevens Institute of Technology
Delivering enterprise AI automation projects: 85% cycle time reduction for Fortune 100 compliance workflow, LangGraph multi-agent systems for conference planning, image-generation pipelines for retail styling, multimodal recommendation engines using embeddings and engagement signals.
Healthcare Operations
Supported strategy and analytics efforts across 18 hospitals in 6 countries, focusing on pricing, capacity, and performance. Work contributed to EBITDA gains (~$14M) and revenue growth (~$20M), including post-acquisition integration.
Featured projects
See all projects →Writing
The gap between a working proof-of-concept and a production AI system is not a technical problem. It is an operating-model problem — and most enterprises are paying for it twice: once in sunk capital, and again in the competitive ground they cede every quarter the system does not ship.
In modern enterprises, the real constraint is not compute, but clarity. As systems scale, ambiguity compounds faster than any technical limitation.
What actually breaks in retrieval-augmented generation when you move from prototype to production — and how to fix it.
On LinkedIn
Follow →The gap between a working proof-of-concept and a production AI system is not a technical problem. It is an operating-model problem — and most enterprises are paying for it twice: once in sunk capital, and again in the competitive ground they cede every quarter the system does not ship.
Read on LinkedIn →
In modern enterprises, the real constraint is not compute, but clarity. As systems scale, ambiguity compounds faster than any technical limitation.
Read on LinkedIn →
Stack
About
Sanjeet Mishra is an experienced analytics and business professional with over a decade of expertise in strategy, operations, and financial analysis. He is skilled in AI engineering, process redesign, and advanced analytics, and is currently pursuing an MS in Business Analytics & AI at Stevens Institute of Technology.
His professional experience includes roles at Stevens Institute of Technology, Kauvery Hospital, KIMS Healthcare, ZS Associates, Mahindra Holidays & Resorts, and Wipro Technologies. At Stevens, he has worked with Fortune 100 clients using AI technologies and contributed to their AI projects.
Sanjeet holds an MBA from IIM Kozhikode and a bachelor's degree from Manipal Institute of Technology.
His skills include growth strategy, financial modeling, AI/ML technologies, and platforms like Python, SQL, and AWS SageMaker.
Background
Education
- MS candidate in Business Analytics & AI, Stevens Institute of Technology, USA
- MBA, IIM Kozhikode
- Bachelor's degree, Manipal Institute of Technology
Get in touch
Available for consulting, partnerships, and exciting projects.
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