Director of Engineering

UiPath UiPath · Enterprise · Bangalore, India · Engineering

Director of Engineering for UiPath's Vertical Solutions team, focusing on leading strategic AI initiatives, defining vision, driving execution, and scaling the engineering organization. The role requires deep technical expertise in modern AI architectures, full-stack systems, and people leadership to build and scale production-grade AI applications leveraging LLMs, agents, and orchestration frameworks.

What you'd actually do

  1. Define and execute the engineering vision, and architectural decisions facross backend, frontend, and AI products
  2. Lead the development of production-grade AI applications leveraging LLMs, agents, tool-calling frameworks, and workflow orchestration systems.
  3. Drive adoption of modern AI engineering practices including evaluation frameworks, regression testing, observability, and human-in-the-loop workflows.
  4. Guide teams in designing agentic systems utilizing LangGraph, LangChain, or equivalent orchestration frameworks.
  5. Build, mentor, and scale high-performing engineering teams across multiple product areas.

Skills

Required

  • 15+ years of software engineering experience
  • 8+ years leading engineering teams
  • building and scaling enterprise-grade distributed systems
  • cloud-native platforms
  • Python
  • C#
  • Java
  • JavaScript
  • developing production AI systems
  • LLMs
  • agents
  • orchestration frameworks
  • evaluation methodologies
  • system design
  • software architecture
  • distributed computing
  • concurrency
  • scalability patterns
  • React
  • TypeScript
  • Azure
  • AWS
  • GCP
  • Docker
  • Kubernetes
  • microservices
  • DevOps practices
  • driving engineering transformation
  • AI-powered development tools
  • communication skills
  • stakeholder management

Nice to have

  • Healthcare Technology
  • Financial Technology
  • Procurement Technology
  • enterprise SaaS domains
  • Retrieval-Augmented Generation (RAG)
  • vector databases
  • retrieval systems
  • citation-grounded AI workflows
  • applied machine learning techniques
  • classification
  • anomaly detection
  • ranking
  • predictive modeling
  • evaluation-driven AI systems
  • quality measurement frameworks
  • large-scale data platforms
  • Snowflake
  • data warehouses
  • analytical ecosystems
  • leading globally distributed engineering organizations

What the JD emphasized

  • production-grade AI applications
  • agentic systems
  • evaluation frameworks
  • orchestration frameworks
  • AI-native product development

Other signals

  • leading AI initiatives
  • production-grade AI applications
  • agentic systems
  • AI-native product development