AI/ML-focused hands-on engineering leader with a track record of building and scaling product-driven startups and hypergrowth platforms. Deep experience across the stack—from business ideation to backend, frontend, infrastructure, and AI/ML. Proven ability to lead 0–1 and 1–10 journeys with both strategic and tactical depth. Led engineering at Simpl (India's first BNPL) and scaled Rippling's platform from $250M to $11B. Passionate about developer experience, systems design, and building strong engineering cultures. Actively contributes to the tech community through OSS, mentorship, and thought leadership.
- One of four founding members of the Applied ML team, building foundational AI/ML infrastructure at scale
- Built and launched Talent Signal, Rippling's first AI-powered product
- Shaped Rippling's AI/ML platform strategy, making it accessible across multiple product teams
- Led the core platform team fueling growth from $250M to $11B valuation as engineering grew from 60 to 600
- Solved scaling challenges in architecture, developer experience, and infrastructure
- Built and operationalized Rippling's India engineering hub with robust hiring and performance frameworks
- Led engineering from the early days of India's first BNPL product as transactions grew 100x
- Scaled a technically complex B2B2C fintech platform spanning mobile SDKs, APIs, web, mobile, and ML
- Grew the engineering team from 3 to ~30 while maintaining a culture of speed and technical excellence
- Speaker at meetups and conferences on microservices, developer experience, and AI
- Author of open-source tool cloudlift
- Published blog posts: Containerizing Django at Rippling, Remote Dev Setup
- Open to mentoring, especially in AI, infra, and early-stage startup building
Took a hands-on leadership role to kickstart AI/ML initiatives at Rippling.
- Designed and implemented the ML Infra for Ads platform; reduced model launch time from 7 days to 4 hours
- Built and launched Talent Signal, Rippling's first AI-powered product
- Established the Applied ML foundations now used by multiple teams to develop and deploy AI features across Rippling's product suite
- Worked on AI/ML and MLOps setup: model data pipelines, model building, Databricks setup, IaC
- Partnering with multiple teams to launch their AI products
Led the core platform architecture, overseeing engineering growth from ~60 to ~600 members.
- Managed a shared services organization, scaling it to a ~90-member team
- Reduced deployment times from ~60 minutes to ~10 minutes
- Resolved critical issues related to site slowdowns, deployment bottlenecks, and scaling challenges, ensuring 99.99% uptime
- Modernized application frameworks, in-house background job runner, and CI/CD pipelines
- Scaled a monolithic architecture through the introduction of execution namespaces
- Implemented observability tools (Datadog, structured logging, API and background job instrumentation)
- Stabilized the test suite, improving feedback times by 2x while reducing costs
- Upgraded the monolithic Python runtime from Python 2 to Python 3
- Scaled database infrastructure vertically and horizontally by splitting the monolithic DB using execution namespaces
- Led initiatives around containers, Kubernetes, CI/CD pipelines, database infrastructure, and developer experience
- Developed a cloud-based remote development environment for engineers
- Designed and implemented a company-wide event platform used across all Rippling applications
Led engineering for India's first BNPL product, scaling transactions 100x and the team from 4 to 30.
- Architected and scaled the platform to handle 100x scale
- Moved to a polyglot microservices architecture to handle synchronous and asynchronous workloads
- Implemented event-sourcing architecture with Kafka
- Worked on data pipelines
- Coached team to follow XP principles (TDD/BDD, CD)
- Conceived, built, and open-sourced cloudlift, a devops tool for AWS ECS deployments
- Scaled up the engineering team from 4 to 30 while ensuring excellent engineering culture
- Created functional pods aligned with architecture (Conway's law)
- Given talks at meet-ups and conferences
Worked on projects of different scale, domain, and tech-stack across multiple clients.
- Built a virtualization platform (similar to AWS EC2) with Citrix Xen as the underlying layer, using Ruby, for a US client
- Built a multi-tenant railway booking application handling >90% of traffic in the UK
- Built a workflow system for a publishing services company
- Built a single page application that disburses a personal loan in a day for a leading European bank
- Built one of India's famous online movie ticket booking platforms using GoLang microservices
Developed software for enterprise clients in the automotive sector.
- Developed a module in a supply chain management solution for a leading automobile manufacturer
- Designed to be flexible and deployed in multiple countries
- Ran webinars internally to upskill the team
Open-source DevOps tool for managing containerized services on AWS ECS, conceived and built while at Simpl.
- Built to automate container deployments to AWS ECS
- Open-sourced; widely adopted by teams deploying on AWS