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Sapan Kulshrestha

Principal Applied Scientist at Amazon

Generative AI (LLMs)Agentic AICausal InferenceRecommender SystemsBig Data AnalyticsStrategic Planning
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About

I'm Sapan Kulshrestha, a Principal Applied Scientist at Amazon with over 12 years of experience building large-scale AI systems. My career has evolved from data engineering and BI at Oracle to leading science competencies for Alexa and Amazon's marketplace. Currently, I focus on GenAI and Agentic AI, specifically how autonomous agents can transform commerce and everyday essentials. I am passionate about the intersection of technical research and real-world business impact, having led initiatives that drive significant free cash flow. Beyond my core role, I am an IEEE Senior Member, author, and mentor who cares deeply about responsible AI and the future of shopping agents. I’m here to share technical insights, mentor the next generation of scientists, and collaborate with others who are pushing the boundaries of what AI can do in the e-commerce space.

Networking

What I can offer

  • ›Technical mentorship in AI/ML
  • ›Insights on scaling GenAI systems
  • ›Expertise in e-commerce science and incrementality measurement
  • ›Strategic advice on AI product delivery

Looking for

  • ›expanding my professional network
  • ›exploring mutual opportunities in AI and e-commerce
  • ›connecting with researchers and product leaders for collaboration

Best fit for

AI researchersProduct leadersAI enthusiastsCareer seekers and interns in the tech community

Current Interests

Agentic CommerceMulti-agent architecturesModel disagreement learningResponsible AI and safetyHardware scaling (e.g., Trainium3)

Background

Career

Started as a Software Engineer at NTT DATA, moved to BI and Data Warehousing at Oracle, then spent over 12 years at Amazon progressing from Data Engineer to Principal Applied Scientist.

Education

B.E. in Information Science & Engineering, Ramaiah Institute Of Technology (2002 – 2006); PUC in Science, Seshadripuram Pre University College (2000 – 2002).

Achievements

  • ›Led initiatives driving hundreds of millions of dollars in Free Cash Flow (FCF) at Amazon.
  • ›Founding member of the Seller Recommendations program contributing to Amazon's 2016 performance.
  • ›Established foundational measurement systems for incrementality and marketing mix models.
  • ›IEEE Senior Member and Advisory Board member.

Opinions

  • Fine-tuned smaller models can match frontier models for specific tasks; the moat is domain-specific data.
  • The ceiling for AI agents is retrieval quality and structured data, not raw reasoning power.
  • Safety gaps make unsupervised financial decisions by AI currently untenable.
  • Autonomous agents will become the primary way companies extract value from AI.