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Join Coursera's fast-paced innovation team as a Senior Software Engineer focused on building and deploying AI-powered solutions for enterprise and campus customers. This role sits at the intersection of AI/Data Engineering, cloud architecture, and customer-facing solutioning, requiring hands-on work with customers to map workflows, prototype solutions, and deploy production systems.
You will operate across the full engagement lifecycle: scoping customer environments and pain points like a consultant, rapidly prototyping working demos in real time with customers, and hardening validated solutions into production-grade, secure, and compliant deployments. This is a customer-facing role requiring regular travel to customer sites for discovery, prototyping, and go-live phases.
Key responsibilities include:
- Scope customer environments directly with executive sponsors and IT/data owners, mapping systems and workflows to identify real business problems
- Rapidly prototype and demo working solutions, iterating in real time to prove value
- Bridge customer needs and core engineering to harden prototypes into production deployments
- Design multi-tenant, hybrid, or customer-controlled deployment architectures based on data residency and compliance requirements
- Build and own identity/access management, encryption, and secure connectivity (mTLS, VPC peering, PrivateLink) for customer-embedded deployments
- Integrate security and compliance considerations into discovery conversations early
- Own CI/CD, observability, and production support for systems in customer environments
- Identify repeatable patterns across engagements and feed insights back to Product
- Collaborate with Product Managers, AI Specialists, and Program Managers on problem scoping
- Travel to customer sites regularly for workshops, scoping, prototyping, and executive readouts
Required qualifications: 5+ years software engineering with strong backend and cloud infrastructure experience; 1+ years building production-grade agentic AI solutions; proficiency in Python, Java, TypeScript, Docker, Kubernetes, Kafka; deep cloud platform knowledge (AWS preferred); strong data engineering fundamentals; working knowledge of IAM, encryption, and secure network patterns; demonstrated comfort operating directly with customers on ambiguous problems; willingness to travel regularly.
