Software Engineer 2 (India)

UiPathYesterday

Location

Bangalore - Engineering

Type

Full Time

Salary

INR 1,200,000 – 1,800,000

Level

Mid

Role

Backend Engineer

Posted

Jul 24, 2026

Full TimeMid

The role

Summary

Join UiPath's Vertical Solutions team as a Software Engineer 2 to build cutting-edge AI agentic orchestration platforms recognized as our Invention of the Year for two consecutive years. In this high-impact role within our Act 2 strategy, you'll design and develop full-stack distributed systems powering enterprise AI automation, combining large language models, multi-agent architectures, and human-in-the-loop workflows. This position requires 3+ years of software engineering expertise in distributed systems and backend architecture, with proficiency in Python, C#, React, and production AI system development including LLMs, orchestration frameworks, and evaluation methodologies.

What you'll do

Design and Develop Full Stack Solutions: Architect and implement full-stack systems for Vertical Solutions products leveraging AI coding assistants. Create scalable backend services using Python and C#, design REST/GraphQL APIs, and integrate with React-based frontends to deliver comprehensive enterprise automation solutions.

Build Production AI Systems: Engineer production-grade systems incorporating large language models (LLMs), tool-calling mechanisms, and multi-agent architectures. Implement orchestration frameworks such as LangGraph and LangChain, handle structured outputs, and design evaluation systems for real-world AI workflows.

Develop Distributed Systems Infrastructure: Design and maintain highly scalable, reliable distributed systems with focus on idempotency, replay-ability, state management, and long-running job orchestration. Build resilient backend services deployed on cloud platforms (Azure, AWS, GCP) using containerization and Kubernetes.

Rapid Prototyping and Customer Validation: Convert business ideas into working prototypes rapidly, validate concepts with real customers, and iterate based on feedback. Move from proof-of-concept to production systems efficiently within short development cycles, adapting quickly to evolving product requirements.

Measure and Optimize System Quality: Design comprehensive evaluation frameworks and metrics for non-deterministic AI systems. Measure system quality against real customer data, establish baselines for model behavior, and make evidence-based decisions on shipping criteria, avoiding vanity metrics.

Customer-Centric Problem Solving: Deeply understand end-user needs and business contexts for features built. Translate complex customer requirements into elegant, maintainable technical solutions. Collaborate with stakeholders across healthcare, finance, and procurement domains to deliver targeted value.

Implement DevOps and Infrastructure Practices: Establish and maintain CI/CD pipelines, infrastructure as code, and containerization best practices. Build deployment automation, monitoring systems, and operational excellence practices that support rapid iteration and high reliability for enterprise customers.

Lead AI Tool Adoption and Productivity Enhancement: Drive team adoption of AI-powered development tools (GitHub Copilot, Claude Code, Cursor). Set usage norms, measure productivity impact, and iterate on integration patterns to enhance engineering velocity across the team.

What we look for

Technical

Full Stack Development ProficiencyStrong expertise in backend systems (Python and/or C#) and frontend frameworks (React with TypeScript). Ability to design and implement complete systems from database layer through REST/GraphQL APIs to user interfaces.

Distributed Systems ArchitectureDeep understanding of distributed system principles including idempotency, replay-ability, state management, and long-running workflow orchestration. Experience designing systems that handle failures gracefully and maintain consistency at scale.

Production AI Systems DevelopmentHands-on experience building enterprise-grade AI systems with LLMs, tool-calling capabilities, and multi-agent orchestration. Proficiency with frameworks like LangGraph and LangChain, structured output handling, and evaluation methodologies for model behavior.

Cloud Platform and ContainerizationPractical experience with major cloud providers (Azure, AWS, or GCP). Proficiency with Docker containerization, Kubernetes orchestration, and cloud-native development patterns for scalable system deployment.

System Design and ArchitectureStrong grasp of architectural design patterns, data structures, algorithms, and object-oriented programming principles. Ability to design scalable, maintainable systems that meet both functional and non-functional requirements.

Asynchronous and Concurrent ProgrammingDeep understanding of multithreading, asynchronous programming models, synchronization primitives, and cloud-native concurrency patterns. Experience building systems that efficiently handle high-concurrency scenarios.

AI Development Tools and Workflow IntegrationDemonstrated hands-on experience with AI coding assistants (GitHub Copilot, Claude Code, Cursor). Ability to integrate these tools into daily development workflows and measure their impact on engineering velocity.

Education

Bachelor's or Master's DegreeBachelor's degree in Computer Science, Engineering, or related technical field. Master's degree is advantageous but not required; equivalent professional experience in software development is acceptable.

Experience

3+ Years Software Engineering ExperienceMinimum three years of professional software engineering experience with focus on distributed systems and backend architecture. Experience building production systems that serve enterprise customers at scale.

System-Level Language ProficiencyStrong production experience with one or more system-level languages including C#, Java, JavaScript, or Python. Demonstrated ability to write high-quality, maintainable code in multiple language paradigms.

Full Stack Development BackgroundProven ability to work across the full stack including backend services, REST/GraphQL API design, component contracts, and basic UI integration. Experience contributing meaningfully to both backend and frontend layers.

Agile and Modern Development PracticesHands-on experience with agile development methodologies, CI/CD pipeline implementation, DevOps practices, and infrastructure as code. Comfort working in fast-paced, iterative development environments.

Industry Domain Experience (Preferred)Experience working on healthcare technology, financial technology, or procurement technology systems represents significant competitive advantage. Understanding of regulatory requirements and domain-specific challenges in these sectors.

Skills

Required skills

Python or C# Backend DevelopmentProduction-level proficiency in Python for agent APIs, ETL pipelines, and AI system development, or C# for core platform development. Understanding of framework ecosystems and best practices for scalable backend systems.

React and Modern Frontend FrameworksPractical experience with React and TypeScript for building interactive user interfaces. Understanding of component architecture, state management, and integration with REST/GraphQL backends.

Distributed Systems DesignCore competency in designing fault-tolerant distributed systems with emphasis on consistency, availability, and partition tolerance. Experience implementing idempotent operations and managing distributed state.

LLM and Multi-Agent SystemsHands-on experience building with large language models, implementing tool-calling mechanisms, designing multi-agent orchestration patterns, and structuring AI system outputs for enterprise reliability.

Orchestration FrameworksPractical experience with workflow orchestration frameworks such as LangGraph, LangChain, Temporal, Airflow, or equivalent. Understanding of state machines, DAG execution, and distributed job coordination.

API Design and ImplementationStrong experience designing and implementing scalable APIs using REST or GraphQL. Understanding of API versioning, authentication, rate limiting, and documentation practices for enterprise systems.

Cloud InfrastructureHands-on experience deploying and managing applications on cloud platforms (Azure, AWS, or GCP). Knowledge of managed services, serverless architectures, and cost optimization practices.

Container OrchestrationPractical experience with Docker containerization and Kubernetes orchestration for managing distributed applications. Understanding of pod lifecycle, services, deployments, and configuration management.

Quantitative Analysis and MetricsStrong analytical thinking with ability to reason quantitatively about system quality. Skill in designing meaningful metrics, avoiding vanity numbers, and using data to drive architectural decisions.

AI Coding Tools IntegrationDemonstrated proficiency with AI coding assistants (GitHub Copilot, Claude Code, Cursor) as part of daily development workflow. Ability to leverage AI tools for code generation, debugging, and productivity enhancement.

Nice to have

Healthcare, Finance, or Procurement Tech ExperienceDomain expertise in healthcare technology (EHR, clinical workflows), financial technology (payments, trading, compliance), or procurement systems (sourcing, vendor management). Understanding of regulatory requirements (HIPAA, PCI-DSS, SOC2) in these sectors.

Data Science and ML EvaluationExperience with data science fundamentals, statistical analysis, and machine learning evaluation methodologies. Ability to assess model performance, run experiments, and interpret results in business context.

Applied Machine Learning TechniquesHands-on experience implementing classification, anomaly detection, ranking systems, or predictive modeling. Understanding of feature engineering, model training, and deployment pipelines.

AI System Evaluation FrameworksExperience designing experimentation frameworks and establishing metrics for evaluating AI systems. Proficiency in implementing regression testing for LLM outputs and human-in-the-loop review workflows.

Large-Scale Data PlatformsExperience working with Snowflake, columnar warehouses (Parquet, ORC), or denormalized data models. Understanding of data modeling for analytics and query optimization at scale.

Retrieval Augmented Generation (RAG) SystemsFamiliarity with RAG architectures, vector databases, semantic search, and citation-grounded AI workflows. Experience implementing retrieval-heavy systems for enterprise applications.

Trustworthy AI EngineeringExperience blending deterministic rules engines with probabilistic AI systems. Knowledge of designing systems for explainability, bias detection, and human-in-the-loop review mechanisms.

Platform and Product DevelopmentExperience in startup environments, applied research, or 0-to-1 product development. Comfort with ambiguity, rapid prototyping, and shipping features with incomplete information.

Open Source ContributionsActive participation in open-source projects, particularly in AI, distributed systems, or DevOps domains. Demonstrated ability to collaborate with distributed teams and contribute to community-driven development.

Workflow Orchestration PlatformsExperience with Temporal, Airflow, BPMN-based platforms, Step Functions, or Maestro. Understanding of workflow definition, state transitions, and failure recovery in orchestration systems.