Sai Sreenivas Kodur
Head of Data and AI Engineering, Infrastructure at Coframe
About
I'm Sai Sreenivas Kodur, currently the Head of Data and AI Engineering at Coframe. My career has been defined by building and scaling high-impact technical systems, from my early days optimizing search at Myntra and Zomato to co-founding Spoonshot, which we successfully scaled and sold to Target Research Group. I’m deeply passionate about the transition from simple AI models to autonomous agents and believe that truly successful AI products must be built 'AI-native' on a bedrock of solid systems engineering. Whether I'm leading a 'cracked team' of engineers or advising startups on MLOps and data infrastructure, I focus on driving measurable business outcomes like incremental revenue and performance optimization. I’m always looking to connect with fellow data geeks and researchers who are pushing the boundaries of what’s possible in the AI and infrastructure space.
Networking
What I can offer
- ›Technical advisory for enterprise accounts
- ›Strategic planning for startups
- ›Expertise in scaling data infrastructure and MLOps
- ›Insights into Food Tech and data intelligence
Looking for
- ›Data geeks
- ›Growth strategists
- ›AI researchers
- ›exploring mutual opportunities in Data and AI Engineering
Best fit for
Current Interests
Background
Career
Began as a software engineer in search at Zomato and Myntra, co-founded and served as CTO of Spoonshot (acquired by Target Research Group), transitioned into engineering leadership at Observe.AI, and currently leads Data and AI Engineering at Coframe.
Education
Dual Degree (Bachelors + Master of Technology) in Computer Science & Engineering from Indian Institute of Technology (IIT), Madras.
Achievements
- ›Co-founded Spoonshot and exited via acquisition by Target Research Group in 2023.
- ›Developed an agentic AI debugger that root-caused 77% of on-call tickets.
- ›Scaled search systems to 300K RPM on a 1M+ item catalog at Myntra.
- ›Built a web scraping platform processing 50M+ pages per day.
- ›Scaled engineering teams up to 45+ people.
Opinions
- Strong system and engineering foundations are a prerequisite for any successful AI product.
- Build 'AI native' products rather than just layering AI onto existing systems.
- High premium on 'cracked teams' (competitive programmers, former founders) to raise the hiring bar.