Experian is hiring a Gen AI Engineer

We are looking for an enthusiastic Senior GenAI Engineer to drive our generative AI initiatives, with a strong focus on building GenAI utilities that elevate Quality Assurance practices and accelerate developer productivity across the engineering organisation. You will design, build, and operationalise LLM-powered tools, agents, and platforms on AWS that are secure, scalable, and production-ready in a regulated environment. Key Responsibilities - You will be #LI-hybrid based in Hyderabad and reporting to Director Engineering - Work as a senior GenAI engineer in an agile team to deliver high-quality, production-grade GenAI solutions within agreed timelines, aligned with business requirements and agile principles. - Design and build GenAI-powered utilities for QA - including intelligent test case generation, test data synthesis, automated defect triage, log/anomaly summarization, and assistive code/test review tooling - to measurably improve quality and engineering velocity. - Develop reusable GenAI accelerators, libraries, and internal copilots that increase productivity across software engineering, QA, and operations teams. - Translate business and engineering requirements into clean, scalable Python code, applying design patterns, secure coding practices, and modern LLM application architectures (RAG, tool-use, agents). - Build and operate LLM applications on AWS using services such as Amazon Bedrock, SageMaker, Lambda, ECS/EKS, API Gateway, S3, DynamoDB, and OpenSearch. - Establish patterns and frameworks for prompt engineering, retrieval-augmented generation, evaluation, observability, and continuous improvement of GenAI systems. - Champion responsible AI - guardrails, PII protection, model governance, hallucination mitigation, and compliance with security and regulatory requirements. - Collaborate closely with cross-functional team members - QA engineers, software engineers, architects, product, and security - to design, develop, test, and release GenAI-powered software. - Contribute to development processes and practices, fostering a culture of continuous integration, delivery, evaluation, and improvement for GenAI systems. - Provide clear and concise documentation for prompts, models, datasets, code, processes, and system architecture to support knowledge sharing and maintainability.