Senior Engineering Manager (AI) at Experian

Hyderabad, Telangana, India -

Full Time

Start Date

Immediate

Expiry Date

25 Jun, 26

Salary

0.0

Posted On

27 Mar, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Systems Engineering, Platform Strategy, LLM, Conversational Experiences, Model Context Protocol (MCP), Cloud Services, AWS, IaC, AI-Driven Software Engineering, Prompt Lifecycle Management, Observability, Architectural Standards, Security, Reliability, Compliance, Technical Leadership

Industry

Information Services

Description

Company Description Experian is a global data and technology company, powering opportunities for people and businesses around the world. We help to redefine lending practices, uncover and prevent fraud, simplify healthcare, create marketing solutions, and gain deeper insights into the automotive market, all using our unique combination of data, analytics and software. We also assist millions of people to realize their financial goals and help them save time and money. We operate across a range of markets, from financial services to healthcare, automotive, agribusiness, insurance, and many more industry segments. We invest in people and new advanced technologies to unlock the power of data. As a FTSE 100 Index company listed on the London Stock Exchange (EXPN), we have a team of 22,500 people across 32 countries. Our corporate headquarters are in Dublin, Ireland. Learn more at experianplc.com. Job Description Job description We are looking for a collaborative and customer focused Senior Engineering Manager in AI to work within the ECS Technology function; a technically strong engineering leader who manages people effectively while staying deeply engaged in architectural decision‑making, AI systems engineering, platform strategy and AI‑driven software engineering practices. This is a hands‑on technical leadership role: you will set engineering direction, define platform approaches and ensure the scalability and maintainability of LLM‑based conversational experiences. Who we are Experian Consumer Services (ECS) are the team behind your Experian Free Credit Score, Experian Boost and the advanced Credit Expert product. We are an agile business, that has been through a complete technical and organisational transformation. Why this role is important to us The role is platform‑minded. The teams you lead will build and operate foundational capabilities, patterns and tooling that enable other product‑engineering teams to contribute features into AI surfaces - from our on-property chatbot EVA to off-property integrations such as OpenAI Apps and beyond. Success is measured not just by what your own teams deliver directly, but by how effectively other teams can build on the platform you provide, and by the breadth of functions across the business that benefit from the reusable agentic capabilities your teams create. You will work closely on Model Context Protocol design and implementation - owning the design and creation of MCP servers that power on‑ and off‑property experiences - and partner with Cloud Services (internal IaC) and AWS to ensure secure, scalable deployment models for conversational AI systems. An explicit part of this role includes driving AI‑driven software engineering across teams: identifying opportunities to use LLMs and automation to accelerate development workflows, improve engineering throughput, reduce toil, and strengthen overall engineering effectiveness. Key Responsibilities Lead engineering teams building LLM‑based conversational capabilities and platform components that enable contribution from multiple domains. Design, build and operate MCP servers (Model Context Protocol) to provide consistent, secure and extensible interfaces into on‑ and off‑property conversational channels. Define and evolve platform patterns, abstractions, SDKs and extension points that let other teams build into conversational channels with minimal friction. Drive adoption of AI‑driven software engineering practices, including AI‑assisted coding, code review, test generation, operational automation and developer‑tooling enhancements. Partner with Cloud Services (IaC) and AWS to provide well‑governed, scalable infrastructure and deployment tooling. Establish architectural standards that maximise extensibility and long‑term maintainability while avoiding premature complexity. Implement robust LLM‑specific testing and evaluation (regression harnesses, behaviour change detection, safety checks, latency and quality monitoring). Drive engineering practices for LLM systems: retrieval‑augmented generation, prompt lifecycle management, model/provider integration, and platform‑level observability. Enable domain teams via documentation, tooling, examples, training and support; track adoption, time‑to‑first‑feature and developer experience as key metrics. Provide technical coaching and clear expectations for engineering quality across teams. Ensure security, reliability and compliance across conversational systems and platform services. Collaborate with central Experian platform and AI teams and peer engineering leaders across business units to align on shared patterns, reuse capabilities and contribute to group-wide AI strategy. Qualifications Qualifications Qualifications and Experience Proven experience leading engineering teams with strong technical ownership in AI‑enabled or platform systems. Hands‑on experience designing and operating platform components and APIs/SDKs used by multiple product teams. Practical understanding of LLM technologies, evaluation methods, prompting approaches, retrieval techniques and safety considerations. Experience designing and scaling systems on AWS and working with internal platform/IaC teams. Experience applying AI‑driven engineering techniques to accelerate development workflows and reduce engineering friction. Ability to reason clearly about architectural trade‑offs with a focus on platform simplicity, extensibility and sustainability. Strong communication skills for influencing engineers, product teams and platform stakeholders. Track record enabling other teams to build on your platform (measured by adoption, contribution volume, time‑to‑integrate, support load). How Success Will Be Measured Adoption of MCP servers and platform SDKs across product‑engineering domains. Time‑to‑first‑feature for new teams building into conversational channels. Reliability, latency and quality KPIs for conversational experiences. Measured uplift in engineering throughput enabled by AI‑driven engineering tools and practices. Reduction in support load via self‑service docs, tooling and guardrails. Evidence of safe and compliant LLM behaviour (evaluation pass rates, incident rate). Additional Information Our uniqueness is that we celebrate yours. Experian's culture and people are important differentiators. We take our people agenda very seriously and focus on what matters; DEI, work/life balance, development, authenticity, collaboration, wellness, reward & recognition, volunteering... the list goes on. Experian's people first approach is award-winning; World's Best Workplaces™ 2024 (Fortune Top 25), Great Place To Work™ in 24 countries, and Glassdoor Best Places to Work 2024 to name a few. Check out Experian Life on social or our Careers Site to understand why. Experian is proud to be an Equal Opportunity and Affirmative Action employer. Innovation is an important part of Experian's DNA and practices, and our diverse workforce drives our success. Everyone can succeed at Experian and bring their whole self to work, irrespective of their gender, ethnicity, religion, colour, sexuality, physical ability or age. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity. Experian Careers - Creating a better tomorrow together Find out what its like to work for Experian by clicking here Employee Status: Regular Role Type: Hybrid Department: Technology Schedule: Full Time

Responsibilities

The manager will lead engineering teams building LLM-based conversational capabilities and platform components, focusing on designing, building, and operating MCP servers for consistent interfaces. They will also drive the adoption of AI-driven software engineering practices to accelerate development workflows and improve engineering effectiveness across teams.