How To Get Hired For Agentic AI Big Tech Roles in 2026 (Amazon Sr Data Scientist POV) - w/ Surya

Naman introduces Surya Kari, a senior generative AI data scientist at Amazon who works with Fortune 500 companies and frontier AI models. The episode asks what AI skill actually looks like beyond using Claude or Copilot as autocomplete, how agentic systems differ from ordinary pipelines, and what students should learn if they want durable AI careers.

Surya's role is deeply customer-facing. He speaks with organizations that want to build foundation models, domain-specific systems, brand chatbots, reinforcement-learning workloads, or multilingual mixture-of-experts models. Some customers need strategic direction; others need his team to help build the first working version. The common task is translating a real business problem into a viable generative AI system.

Those customers range from Fortune 500 companies to small startups and advanced AI labs. Some already have data-science teams and need to test a newly released model. Others have decades of proprietary data and want a smaller domain model for their own customers. Surya describes the work as a white-glove service because the technical path changes with each organization's data, people, and goal.

Most of Surya's current learning happens on the job because models and methods move too quickly to separate learning from delivery. Earlier in his career, Udacity and Udemy courses helped him go deep into specific subjects. He still sees value in structured courses, but warns that a model or technique can appear between one customer conversation and the next.

Surya says the difference is not whether someone uses an AI coding tool; it is whether they can reason about the output. If a model proposes an architecture, the engineer should know whether it makes sense. If it generates a large codebase, the engineer should understand how the modules connect. Otherwise a production incident leaves the team with code that nobody can diagnose.