Senior Software Engineer -AI

The people at UiPath believe in the transformative power of automation to change how the world works. We’re committed to creating category-leading enterprise software that unleashes that power. To make that happen, we need people who are curious, self-propelled, generous, and genuine. People who love being part of a fast-moving, fast-thinking growth company. And people who care—about each other, about UiPath, and about our larger purpose.

About the role

At UiPath, we're building the next generation of enterprise AI and automation, where intelligent agents, people, and software work together to solve complex business challenges. We're looking for an exceptional Senior Software Engineer to help build the foundational technologies powering our AI platform. You'll work at the intersection of distributed systems, generative AI, and enterprise software, developing capabilities that enable AI agents and intelligent applications to operate reliably, learn continuously, and deliver meaningful business outcomes at scale. This is a hands-on engineering role for someone who loves solving difficult technical problems, thrives in ambiguity, and wants to build products that push the boundaries of what's possible with AI. You'll contribute to critical platform capabilities spanning AI evaluations, agent memory, continuous learning, orchestration, intelligent document processing, and retrieval systems, helping transform emerging AI technologies into reliable, production-ready enterprise solutions. We're looking for engineers who don't just experiment with AI, but have personally built, shipped, and operated AI-powered products in production.

Enterprise AI is moving beyond experimentation. The next generation of intelligent applications must be reliable, secure, scalable, and capable of improving over time. At UiPath, you'll help build the engineering foundation that makes this possible.

Responsibilities

• Build next-generation AI capabilities: Design, develop, and scale services supporting AI agents, evaluations, memory, context management, continuous learning, and intelligent document processing.
• Engineer production-grade GenAI systems: Build and operate LLM-powered applications, retrieval-augmented generation (RAG) pipelines, agentic workflows, and document understanding solutions that solve real customer problems.
• Solve complex distributed-systems challenges: Develop highly available, scalable, cloud-native services with strong foundations in asynchronous processing, concurrency, fault tolerance, security, and performance.
• Make AI reliable and measurable: Develop evaluation frameworks, observability, monitoring, and feedback mechanisms that improve AI quality, accuracy, reliability, and performance over time.
• Reflection and Learning: Build systems for continuous learning across multiple modalities, including agents, ontologies, and deterministic logic.
• Own engineering solutions end to end: Take ideas from ambiguous requirements through technical design, implementation, testing, deployment, and ongoing production operations.
• Optimize for enterprise scale: Address challenges involving multi-tenancy, permissions, retries, idempotency, cost efficiency, latency, and system resilience.
• Build across the stack: Contribute to frontend, backend services, APIs, and product-facing experiences, collaborating across technologies to deliver cohesive solutions.
• Partner closely with customers and product teams: Work alongside Product Managers, Forward Deployed Engineers, and other engineering teams to understand customer challenges and translate them into scalable product capabilities.
• Raise the engineering bar: Influence technical decisions, contribute to architecture discussions, mentor fellow engineers, and help establish best practices for building reliable AI-powered software.

Requirements

• Strong professional software engineering experience building, shipping, and operating complex, production-grade applications, ideally within enterprise SaaS or large-scale cloud platforms.
• Deep understanding of distributed systems, backend architecture, microservices, APIs, and cloud-native development.
• Demonstrated hands-on experience building and deploying production GenAI applications, such as LLM-powered products, RAG systems, AI agents, intelligent document processing, or AI-driven enterprise workflows.
• Strong programming skills in Python, C#, or Java, with experience in TypeScript or an interest in developing full-stack capabilities.
• Experience designing systems for scalability, reliability, performance, security