Coursera + Udemy Offices

Own enterprise AI customer deployments from discovery and product scoping through delivery, adoption, and value realization. Translate customer workflows into product requirements, technical solutions, deployment roadmaps, and measurable outcomes. Partner with engineering, sales, customer success, finance, legal, security, and operations while evaluating AI trade-offs, business value, and production readiness. Identify reusable customer needs and help establish scalable product, operating, and deployment playbooks.

Build and scale production-grade agentic AI infrastructure and platform capabilities. Lead architecture, technical design, reliability, scalability, data privacy, and end-to-end delivery. Collaborate with product and innovation teams to define customer solutions, mentor engineers, improve developer experience, and resolve complex technical issues. The role requires backend engineering, cloud infrastructure, APIs, databases, automated testing, infrastructure as code, and CI/CD expertise.

Build applied AI systems and prototypes using LLMs, RAG pipelines, vector databases, agentic workflows, structured extraction, and custom models. Design evaluation frameworks, measure accuracy and grounding, analyze errors, and iterate on solutions. Work with large structured and unstructured datasets using Python, SQL, and cloud data platforms. Partner with product, data, engineering, and business teams to translate ambiguous problems into measurable AI solutions and document architectures, prompts, runbooks, and results.