Jan 2026 – Present · 2 mos · Bengaluru, Karnataka, India

• Leading the design and evolution of high-scale decision and pricing platforms powering customer offers, personalization, and revenue optimization at MakeMyTrip.
• Own the architecture and roadmap for Manthan – the core coupon and promotion decision engine handling high-volume real-time traffic
• Built scalable, low-latency distributed systems to evaluate complex business rules, personalization logic, and dynamic pricing scenarios
• Drove significant infrastructure and compute cost optimizations through performance tuning, intelligent caching, and evaluation strategy redesign
• Introduced AI-driven capabilities for decision automation, diagnostics, anomaly detection, and operational insights
• Partner closely with Product, Revenue, and Business teams to translate business goals into scalable technology solutions that improve conversion and customer experience
• Established observability, debugging, and self-diagnostic frameworks to increase system transparency and reduce issue resolution time
• Led cross-team initiatives across platform, data, and infrastructure to improve system reliability, scalability, and release velocity
• Mentored engineers and drove engineering excellence in architecture, design reviews, and operational ownership
• Focus areas: Distributed Systems, Decision Engines, Personalization, Cost Optimization, AI-assisted platforms, and High-Scale Backend Architecture

Jan 2025 – Feb 2026 · 1 yr 1 mo · Bengaluru, Karnataka, India

Sep 2022 – Feb 2026 · 3 yrs 5 mos · Bengaluru, Karnataka, India

• Led automation strategies for critical projects, reducing testing time by 40%.
• Rapidly scaled service quality, ensuring seamless delivery for millions of users.
• Collaborated across teams to align technical innovation with business goals.
• Develop: Designed and implemented an advanced recommendation system that predicts potential travel destinations for users based on their preferences, travel history, and seasonal trends.
• Enhance: Improved the overall user experience by integrating AI and machine learning algorithms to provide personalized travel suggestions, increasing user engagement and satisfaction.
• Data-Driven Insights: Utilized big data and analytics to fine-tune the recommendation engine, ensuring highly accurate and relevant travel suggestions.
• Collaborative Projects: Worked closely with cross-functional teams, including guiding internal team and UI-UX designers, to integrate the recommendation service seamlessly into the platform.
• Scalability Improvements: Ensured the recommendation service could handle high traffic volumes, particularly during peak travel seasons, by optimizing the underlying architecture.