About the company
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.
Responsibilities
- 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/PrivateLink, 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.
Requirements
- 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 network patterns (VPC peering, PrivateLink, mTLS) for customer-embedded or regulated environments.
- Demonstrated comfort operating directly with customers — scoping ambiguous problems, running discovery, and demoing work-in-progress solutions live, in person and remotely.
- Willingness and ability to travel regularly to customer sites, domestically and occasionally internationally, as engagement needs require.
- Prior experience leading projects and debugging complex issues with minimal supervision.
- Preferred Qualifications:
- Experience with modern agentic AI tooling such as LangChain, LangGraph, FastMCP, RAG, or MCP.
- Experience with Postgres, DuckDB, pgvector, or similar analytical/transactional data layers.
- Prior experience in a solutions engineering, professional services, or technical consulting role where you owned a customer relationship end-to-end, including on-site engagement.
- Familiarity with data privacy and residency regimes relevant to enterprise/education/government customers (e.g., GDPR, FERPA, DPDPA, HIPAA).
- Demonstrated ability to work in a fast-paced, ambiguous environment and make sound technical trade-offs with limited guidance.
- Excellent communication skills, with the ability to translate technical constraints into terms.