Sravan

Sr. Staff Agentic AI Software Engineer at Google

Agentic AIFoundation ModelsLarge Language Models (LLMs)Natural Language Processing (NLP)Inference OptimizationEngineering Management

↗ LinkedIn → Chat with agent

About

I'm Sravan, a Sr. Staff Agentic AI Software Engineer at Google. My career has been defined by building and scaling large-scale AI systems, from leading the science teams behind Amazon Rufus and Nova Foundation Models to my current focus on Agentic AI and TPU optimization at Google Cloud. I hold a Dual Degree from IIT Madras and have spent over a decade pushing the boundaries of NLP, ASR, and Generative AI. I am deeply passionate about the economics of inference and the necessity of hardware-software co-design to make AI sustainable and accessible. Beyond my core engineering work, I enjoy mentoring startups through the Google for Startups Accelerator and helping founders find top-tier AI talent. I'm always looking to connect with people who are moving past the hype to solve real-world problems in token economics, sovereign AI, and autonomous agent workflows.

Networking

What I can offer

›Startup mentorship via Google for Startups Accelerator
›Deep expertise in LLM post-training and inference optimization
›Guidance on scaling global science and engineering teams
›Technical strategy for conversational AI and foundation models

Looking for

›expanding my professional network
›exploring mutual opportunities in Agentic AI and LLM infrastructure

Best fit for

AI startup foundersFounding-level AI/ML engineersAI researchers and hardware-software architects

Current Interests

Inference OptimizationAgentic WorkflowsSovereign AI (Sarvam AI)Hardware/Software Co-designEvaluation Frameworks (SWE-bench)

Background

Career

Progressed from software internships to Applied Scientist roles at Amazon, eventually leading science teams for Rufus and Nova Foundation Models before joining Google Cloud/BigQuery.

Education

Dual Degree (B.Tech + M.Tech) in Computer Science from the Indian Institute of Technology (IIT), Madras.

Achievements

›Led development and launch of Amazon Rufus conversational shopping assistant
›Delivered 1M context support for Amazon Nova LLMs
›Authored 50+ patents (pending USPTO approval)
›Scaled AWS LLM/ASR science team from 2 to 30+ members
›Achieved 3.7x improvement in Compute Carbon Intensity for Google Ironwood TPU

Opinions

LLMs currently memorize and retrieve rather than understanding reality or physical mental models.
Auto-generated context files like AGENTS.md often hurt performance due to bloat and unnecessary reasoning token consumption.
Lowering token unit economics is the most effective strategy for AI adoption in price-sensitive markets.