Krishna Varma
Lead Site Reliability Engineer @Visa
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WORK HISTORY
Lead Site Reliability Engineer @Visa
Reading, GB
Own site reliability for Visa\'s Tokenization products — one of the most transaction-critical layers of global payment infrastructure, processing billions of events globally• Lead enterprise Kubernetes deployments using Mirantis Container Cloud, managing complex orchestration at a scale most engineers never encounter• Design and maintain incident priority matrices, operational acceptance frameworks, and quarterly capacity reviews for Token applications• Drive production deployments, release management, and real-time troubleshooting of mission-critical payment systems• Lead major product migrations with zero-downtime requirements, applying SRE principles to maintain 99.99%+ availability• Exploring the intersection of SRE and AI infrastructure in practice — published early thinking on semantic caching, actively building with RAGs and LLMs while applying reliability engineering principles to AI system design
EDUCATION
Cochin University of Science and Technology
Bachelor of Technology - BTech, Computer Science And Engineering
Singapore Management University
Master of IT in Business, Business Analytics
ABOUT KRISHNA VARMA
When you tap your card anywhere in the world and the payment goes through in milliseconds — that\'s the kind of infrastructure I build and protect.As Lead SRE at Visa, I own the reliability and scalability of Tokenization products — mission-critical systems operating at global scale. Kubernetes deployments, incident management, capacity planning, release engineering — I\'ve done all of it under the pressure of one of the world\'s largest payment networks.But my focus is shifting — deliberately — toward AI infrastructure.I hold a Professional Certificate in Machine Learning and AI from Imperial College London, and I\'ve been getting my hands dirty with production AI systems. I\'ve begun exploring AI infrastructure in practice — publishing my early thinking on semantic caching and getting hands-on with RAG architectures. I don\'t claim to be an expert yet. But I do bring a perspective most AI learners don\'t have: what it actually takes to keep complex systems alive under real pressure. The domain changes. The rigour doesn\'t. This is the intersection I\'m building my career at: enterprise infrastructure meets production AI.If you\'re working on AI platform engineering, MLOps, or LLM infrastructure at scale — I\'d genuinely love to connect.
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