About the job
Life at UiPath
At UiPath, we are driven by the transformative potential of automation, reshaping how the world operates. Our mission is to develop industry-leading enterprise software that harnesses this power.
To achieve this vision, we seek individuals who are inquisitive, self-motivated, collaborative, and authentic. We thrive on having team members who enjoy being part of a dynamic and rapidly evolving growth company. A core value at UiPath is caring—about one another, our organization, and our broader mission.
Could you be the one to join us?
Your mission
In the role of Forward Deployed Engineer II, you will take charge of delivering tailored customer implementations and transforming real-world business challenges into production-ready agentic automation solutions. You will closely collaborate with customer stakeholders and internal teams to design, develop, integrate, and refine AI-driven workflows, ensuring a balance between speed, reliability, and maintainability. Your practical insights from the field will help inform the organization, enhancing solution patterns, reusable assets, and implementation playbooks.
Unlike traditional software engineers who create a one-size-fits-all product, FDEs deliver customized solutions directly within customer environments, making the UiPath platform relevant to specific business processes and systems. You will work with increasing autonomy, execute well-defined workstreams, and collaborate with senior FDEs and product teams to align with broader technical strategies.
What you'll do at UiPath
Leverage UiPath's automation and orchestration components, such as Maestro, Agent Builder, IXP, Studio, Orchestrator, and Integration Service to independently deliver impactful customer solutions.
Design and develop AI-driven workflows, agents, and automations in accordance with UiPath's architectural and platform standards, requiring minimal oversight.
Utilize practical prompt engineering techniques to effectively guide agent behavior and understand model trade-offs (capability, latency, cost) when selecting LLM providers for various customer use cases.
Enhance platform capabilities using APIs, SDKs, and connectors to meet unique customer needs.
Troubleshoot across systems, including AI inference, data pipelines, orchestration layers, and integration points, resolving most issues independently.

