Forward Deployment Engineer

Responsibilities Build working prototypes and proof-of-concepts (POCs) for Agentic AI use cases across enterprise IT domains Develop platform-agnostic solutions leveraging APIs, LLM frameworks, and cloud-native services Implement lightweight, cost-efficient demo architectures using serverless and managed services Integrate components such as AI agents (reasoning, orchestration, automation), Knowledge fabric / data pipelines, Workflow automation and observability layers Design and build demos aligned to key enterprise domains: Provisioning, monitoring, automation, and scaling scenarios; Service Desk & End User Services; Ticket triaging, virtual agents, self-healing workflows; Network Operations; Event correlation, anomaly detection, automated remediation Build quick-turnaround demo environments for multiple opportunities simultaneously Continuously refine prototypes based on feedback Maintain reusable demo assets, scripts, and templates Ensure demos are scalable, repeatable, and easy to deploy Collaborate with Solution Architects, Sales/business teams, and Platform teams Qualifications and Specialization Bachelor’s degree in Computer Science, IT, Engineering, or related fields Work hands‑on with Agentic AI, multi‑agent systems, and automation workflows Build end‑to‑end prototypes (not isolated features) covering enterprise‑grade architectures (knowledge fabric, orchestration layers, observability) Participate in customer demos, solution positioning, and deal pursuits Develop understanding of customer pain points, create multiple MVPs and demo‑ready prototypes continuously Contribute to a portfolio of innovation assets and reusable IPs Design solutions that work across AWS / Azure / GCP, understand interoperability, abstraction layers, and approaches to avoid vendor lock‑in Develop ability to convert technical implementations into business narratives and demonstrate ROI, efficiency, and transformation outcomes Unique Experience from this Role Work hands‑on with Agentic AI, multi‑agent systems, and automation workflows Build end‑to‑end prototypes (not isolated features) covering enterprise‑grade architectures (knowledge fabric, orchestration layers, observability) Participate in customer demos, solution positioning, and deal pursuits Develop understanding of customer pain points, create multiple MVPs and demo‑ready prototypes continuously Contribute to a portfolio of innovation assets and reusable IPs Design solutions that work across AWS / Azure / GCP, understand interoperability, abstraction layers, and approaches to avoid vendor lock‑in Develop ability to convert technical implementations into business narratives and demonstrate ROI, efficiency, and transformation outcomes Learning Outcomes Convert business problems into solution architecture, working prototypes, and demo‑driven value stories Build complete systems rather than isolated components Learn modern architecture involving knowledge fabric, data pipelines, AI agents, observability, automation, and integration layers Understand how enterprise systems connect and operate together Work in innovation sprint‑based environments with continuous feedback Gain exposure to concepts such as self‑healing systems, predictive incident detection, and automated root cause analysis #J-18808-Ljbffr University of Denver