Infosys's structured internship pairs you with a 10-person team and a real internal-tool project. My team's brief was an enterprise content-discovery service with a GenAI summarization layer. The actual challenge wasn't the AI — it was running a backend at 500+ simulated req/s with the kind of structured logging and input validation that a production environment expects.

1. 500+ req/s sustained · 10-person agile team · delivered 1 week early

   Owned task breakdown, technical decisions, and final delivery within the 10-person team. Built the Flask backend with REST endpoints, secure auth, optimized SQL, and structured logging. Load-tested to 500+ simulated req/s.

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2. GenAI integration: 35% content discovery accuracy lift

   Layered a generative-AI summarization + personalized recommendation surface on top of the search backend. Accuracy of relevant-content retrieval improved by 35% over the keyword baseline; manual curation effort dropped by 40%.

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Working inside a 10-person team — instead of a 2-person duo — taught me about staging changes through code review and pairing on the merge order. Coming back to smaller teams afterwards, I miss the rigor.

• Owned task breakdown and final delivery on a 10-member agile team, shipping a production-ready enterprise Flask backend sustaining 500+ simulated req/s — 1 week ahead of schedule.
• Integrated Generative AI for summaries and personalized recommendations, improving content discovery accuracy by 35%; MVP reduced manual effort by 40%.
• Designed REST endpoints with secure auth, optimized SQL queries with proper indexing, and structured logging — hardened the Flask backend to production-grade specification under sustained load testing.