Teams at Coursera + Udemy

Lead backend engineering initiatives for the Monetization team by designing scalable microservices, APIs, storage layers, and data models. Build and monitor services, conduct A/B testing, analyze metrics, and use insights to guide product decisions. Collaborate with product managers and stakeholders on requirements, technical tradeoffs, architecture, and roadmap priorities while contributing hands-on development and technical leadership.

Build and improve test infrastructure, automation frameworks, CI/CD pipelines, and end-to-end testing strategies. Lead quality initiatives, develop AI-augmented testing solutions, monitor test environments, support incident resolution, and use quality and performance metrics to improve software reliability. Collaborate with Infrastructure, SRE, engineering leaders, and product teams in an agile environment to expand test coverage and enable safe, efficient delivery.

Owns enterprise AI customer deployments from discovery and product scoping through delivery, adoption, and measurable value. Translates customer workflows into product requirements, deployment roadmaps, and technical solutions while partnering with engineering, sales, finance, legal, security, and customer success. Evaluates AI-specific considerations, business cases, risks, and product opportunities, then converts recurring customer needs into scalable capabilities and operating playbooks.