Senior Software Engineer, Applied AI and Customer Solutions

About Coursera + Udemy

Coursera and Udemy are now one company, bringing together two mission-driven brands to create the world's most powerful platform for turning learning into progress. Together, we help more than 300 million learners and 12,000+ enterprise customers build the skills they need for a world being reshaped by AI.

AI is transforming how people learn, work, and grow, and the need for new skills has never been greater. Coursera brings trusted content and credentials from leading university and industry partners, while Udemy brings a dynamic skills marketplace and global network of real-world experts. By combining these strengths, we can connect more people and organizations to the skills they need, when they need them.

By joining our team, you'll have the opportunity to reshape how the world learns and applies skills—and help millions of people participate in the new economy. Bring your ideas, expertise, and perspective to meaningful work that can make a difference at global scale.

As a Senior Software Engineer, you will join a fast-paced innovation team that builds and deploys AI-powered solutions directly with Coursera's enterprise and campus customers. You will sit at the intersection of AI/Data Engineering, cloud and security architecture, and customer-facing solutioning - working hands-on with customers to map their workflows and data, prototype solutions quickly, and harden the ones that prove valuable into production deployments.

You’ll operate across the full engagement lifecycle: scoping a customer's environment and pain points like a consultant, prototyping working demos in real time with the customer, and then hardening the strongest patterns into production-grade, secure and compliant deployments. This role is customer-facing and will require regular travel to customer sites (domestic and occasionally international) for discovery, prototyping, and go-live phases of engagements.

You will work closely with Product Managers, AI Specialists, Data Analysts, and other Engineers on the team, and directly with customer executive sponsors and IT/data owners, to decide what gets standardized, deployed, or retired.

• Scope customer environments directly with executive sponsors and IT/data owners - mapping systems, data models, and workflows to identify the real business problem, not just the stated one
• Rapidly prototype and demo working solutions in front of customers, iterating in real time to prove value fast
• Serve as the bridge between customer & core engineering team to harden validated prototypes into production deployments
• Design and implement multi-tenant, hybrid, or customer-controlled deployment architectures, as per customer's data residency, privacy, and IT-maturity requirements
• Build and own identity and access management, encryption, and secure cross-network connectivity (mTLS, VPC peering/Private Link, API gateways) for customer-embedded deployments
• Bring security, data-residency and compliance judgment into discovery conversations before a commercial commitment is made, not after
• Own CI/CD, observability, and production support for systems living inside customer environments
• Recognize repeatable patterns across customer engagements and feed field evidence back to Product to inform what should be standardized, deployed more broadly, or retired
• Collaborate closely with Product Managers, AI Specialists, and Program Managers to scope problem statements with a laser focus on customer and business impact
• Travel to customer sites as needed (expect regular travel) to support scoping, prototyping, and go-live phases of an engagement, including in-person workshops and executive readouts

• 5+ years of experience in a software engineering role, with strong hands-on backend engineering and cloud infrastructure experience
• 1+ years of experience building production-grade agentic AI solutions
• Proficiency in backend languages such as Python, Java, Typescript and technologies such as Docker, Kubernetes and Kafka with comfort working across the stack
• Deep understanding of cloud platforms (AWS preferred), able to design and operate both multi-tenant and hybrid customer-cloud deployment models
• Strong experience with data engineering fundamentals - ingesting, cleaning, and normalizing messy, inconsistent customer data across disparate source systems
• Working knowledge of identity and access management, encryption/key management, and secure…