AI Engineer

PayU Payments; Gurugram District; Hybrid; INR 1,500,000 - 2,100,000; Full time

Job summary

PayU Finance India is seeking an AI Engineer to ship production AI systems that power lending decisions and operational efficiency. You will own the full lifecycle from evaluating LLM capabilities to deploying robust agents and integrating with internal systems.

Role includes building scalable backend services with Java/Python, implementing RAG pipelines, and ensuring security, observability, and cost efficiency. Hybrid work in Gurugram/Bangalore with global AI tooling exposure.

Qualifications

• Hands-on experience shipping production AI systems.
• Experience deploying AI-powered backend services and agents.
• Experience with LLM capabilities, safety, and governance.

Responsibilities

• Deploy AI agents for lending workflows and integration with internal systems.
• Architect scalable backend services using Java, Spring Boot, and Python.
• Design schemas with SQL/NoSQL for AI workloads and low-latency APIs.
• Build RAG pipelines, tool-use patterns, and safe LLM interaction with data sources.
• Review AI-generated code for correctness, security and tests; establish guardrails.

Skills

Java

Python

Spring Boot

Spring Data JPA

Spring Security

FastAPI/Flask

SQL

NoSQL

REST APIs

Microservices

DDD

Event-driven

AWS

GCP

Azure

Docker

CI/CD

Education

Bachelor's or Master's in CS/AI/Data Science or Mathematics

Tools

Cursor/Copilot

GitHub Copilot

Job description

AI Engineer

Company: PayU Finance India | Experience: 4-7 years | Location: India - Hybrid (Gurugram / Bangalore) | Type: Full-time, IC

About the Role

We need an AI Engineer who ships production AI systems - not prototypes. Youll build AI-powered backend services, autonomous agents, and intelligent integrations that directly impact lending decisions and operational e ciency.

You own the full lifecycle - from evaluating a new LLM capability to deploying an agent that handles real tra c with real financial consequences.

Prior hands-on experience building and deploying AI-powered systems in production is a must.

What You'll Do

Build AI Systems

• Deploy AI agents for lending workflows - document verification, bureau analysis, risk assessment, customer communication
• Build MCP servers and tool integrations extending AI across internal systems
• Implement RAG pipelines over lending policies, regulatory docs, and knowledge bases
• Develop function-calling and tool-use patterns for safe LLM interaction with databases and APIs
• Build evaluation and guardrail frameworks to catch hallucinations and enforce compliance

Engineer the Backend

• Architect scalable services using Java, Spring Boot, and Python for production AI features
• Design schemas using SQL and NoSQL databases optimized for AI workloads
• Build RESTful APIs serving model inference at low latency and high throughput
• Integrate with credit bureaus, banking partners, and payment gateways Raise the Bar

Code reviews for AI patterns and security, optimize token costs and latency, establish AI observability, and drive AI-assisted development (Cursor, Copilot) across the team.

Review and validate AI-generated code for correctness, security, and project standards. Build test suites, guardrails, and eval frameworks for prompts and agent behaviors - with continuous benchmarks, drift detection, and human-in-the-loop review before production.

Required Skills

Backend Engineering

• Languages: Java, Python
• Frameworks: Spring Boot, Spring Data JPA, Spring Security; FastAPI or Flask
• Databases: SQL (schema design, optimization, indexing) and NoSQL (document, key-value, graph)
• APIs & Architecture: RESTful APIs, microservices, domain-driven design, event-driven patterns
• Cloud: AWS (Bedrock, SageMaker, Lambda), GCP (Vertex AI), or Azure (OpenAI, Cognitive Services)
• DevOps: Git, Maven/Gradle, Docker, CI/CD pipelines

AI/ML Engineering

• LLM Integration: Claude, GPT-4, Gemini - API usage, structured output, context management
• AI Agents & MCP: Built agents with planning, tool-use, memory; developed MCP servers and tools
• RAG & Vectors: Retrieval-augmented generation - chunking, embedding, reranking, vector search
• Prompt Engineering: System prompts, few-shot, chain-of-thought, function calling, output parsing
• Evaluation: Eval pipelines - accuracy metrics, human-in-the-loop review, regression detection
• AI Dev Tools: Daily use of Cursor AI, GitHub Copilot, or equivalent

Education

Bachelors / Masters in CS, AI/ML, Data Science, or Mathematics - or equivalent hands-on experience. We value what youve built over where you studied.

PayU is an equal opportunity employer.