AI Engineering Lead

Flutter Entertainment; Hyderabad; Hybrid; INR 4,000,000 - 6,000,000; Full time

Job summary

Flutter Functions’ AI Engineering Lead will shape the technical direction for Flutter’s enterprise AI assistant platforms, guiding an 2–3 member pod of AI developers in building production-grade AI solutions at scale.

You will lead architecture decisions on LLM inference, context engineering, and multi-agent orchestration while mentoring teammates and collaborating with product and cross-functional stakeholders across Flutter’s global operations.

Qualifications

• Bachelor's degree in Computer Science or related STEM field.
• 5+ years of software development experience, with 2–3+ years in AI/ML or agentic systems development.
• Proven leadership experience mentoring technical teams.
• Strong Python backend expertise and production frameworks.
• Hands-on experience with LLM inference systems (Bedrock, LiteLLM).

Responsibilities

• Set the technical direction for enterprise AI assistant platforms, including LLM inference and guardrails.
• Lead deployment, routing, cost optimization, and latency tuning for LLM systems.
• Design scalable context pipelines and prompt architectures at scale.
• Mentor engineers and drive technical standards and reviews.
• Collaborate with product, data science, and platform teams to deliver AI solutions.
• Uphold security-first and Responsible AI governance across Flutter’s ecosystem.

Skills

Python

Backend development

Leadership

AI/ML

LLM inference

Context engineering

Security

Education

Bachelor's degree in CS/AI

Tools

AWS Bedrock

LiteLLM

FastAPI

Flask

Job description

AI Engineering Lead

ABOUT FLUTTER ENTERTAINMENT

Flutter Entertainment is the world’s largest sports betting and iGaming operator with 15.9 million Average Monthly Players worldwide and an annual revenue of $16.4 billion in 2025. We have a portfolio of iconic brands , including FanDuel, Paddy Power, Betfair, PokerStars, Sisal, and Sportsbet.

Flutter Entertainment is listed on theNew York Stock Exchange (NYSE).We’reproud to be recognized by TIME as one of the World’s Best Companies 2025 - a reflection of the passion and commitment of our colleagues across the globe.

Working at Flutter is a chance to work with a growing portfolio of brands across a range of opportunities. We will support you every step of the way to help you grow. Just like our brands, we ensure our people have everything they need to succeed.

FLUTTER ENTERTAINMENT INDIA

Our office in the heart of Hi-Tech City , Hyderabad, serves asFlutter Entertainment’s Global Capability Center (GCC).Now home to 1,000+ talented colleagues, we are shaping the future of entertainment across critical areas such as Customer Support Operations, Data & Technology, Business Operations (Finance, HR, Procurement), and HR Tech & Analytics.

Our Values define us. They are - Build Better Together, Own Every Outcome, Stay Curious and Grow, and Raise the BarEvery day.
They’re how we collaborate, grow, lead, and show up for eachotherevery single day.

With ambitious growthplans for the futureand the global and local scale to achieve them, we continue to serve our customers and care for our communities with the talent and passion of our colleagues.

OVERVIEW OF THE ROLE

Flutter Functions’ AI & Automation team is seeking an experienced you to build and lead a high‑performing pod of 2–3 AI developers. You’ll own the technical vision and execution of Flutter’s enterprise AI assistant and agent platforms — intelligent systems that leverage our vast internal data assets to enhance productivity and decision‑making across our sports betting and gaming operations at global scale.

As an AI Engineering Lead, you’ll combine deep technical expertise with strong people leadership, mentoring a talented team while driving the architectural decisions, technical standards, and engineering practices that power next‑generation AI solutions. You’ll have direct impact on both the platform’s capabilities and the growth trajectory of your team members.

Working in a hybrid environment that blends remote flexibility with collaboration in our modern Hyderabad offices, you’ll partner with product leaders and cross‑functional stakeholders to translate ambitious AI initiatives into production‑grade systems serving over 20 million customers across 20+ regulated markets.

What You’ll Do

Technical Leadership & Architecture

• Platform Vision & Strategy: Establish the technical direction for Flutter’s enterprise AI assistant and agent platforms, making architectural decisions around LLM inference, agentic frameworks, guardrails, and system design that balance innovation with operational excellence.
• LLM Inference & Optimization: Lead the selection, deployment, and optimization of LLM inference systems — LiteLLM, AWS Bedrock , and emerging alternatives — establishing patterns that your team follows, including model routing strategies, cost management, and latency optimization.
• Context Engineering & Prompt Architecture at Scale: Design sophisticated, production‑grade context pipelines and prompt architectures — establishing team best practices, code patterns, and evaluation frameworks that ensure consistency and quality across all platform components.
• Model Tokenomics & Trade‑off Analysis: Mentor your team in understanding and reasoning about token economics, latency budgets, context window trade‑offs, and capability‑to‑cost ratios — embedding this thinking into architectural review processes and technical decisions.
• Guardrails, Safety & Compliance: Architect robust model constraint and guardrail systems aligned with Flutter’s Responsible AI Policy and regulatory obligations. Lead the security‑first mindset across the pod, ensuring AI outputs remain safe, compliant, and auditable across 20+ regulated markets.
• Agentic Framework Mastery: Deep expertise across multiple agent frameworks ( LangChain, LangGraph, Strands, ADK ) — make informed choices about which frameworks and patterns solve which problems, establish team standards, and guide your developers in building sophisticated multi‑agent orchestrations.
• Full‑Stack Problem Ownership: Model end‑to‑end ownership — architecting solutions from LLM integration through backend services to user‑facing interfaces, and cultivating this mindset within your team so complex problems don’t get siloed or handed off prematurely.
• API & Integration Design: Design clean, scalable APIs and integration patterns using AWS services (Bedrock, Lambda, API Gateway), Model Context Protocol (MCP), and modern standards — establish team conventions that make integration predictable and maintainable.

People Leadership & Team Development

• Team Building & Mentorship: Hire, onboard, and actively mentor a pod of 2–3 AI developers — helping them grow from their current level towards technical leadership, fostering a culture of learning, experimentation, and continuous improvement.
• Technical Skill Development: Guide your team through the rapidly evolving AI landscape — mentoring on LLM inference systems, context engineering, agentic patterns, and security practices; helping them stay current with emerging tools and frameworks.
• Code Review & Technical Standards: Establish and enforce technical standards across the pod — conducting rigorous code reviews, catching architectural issues early, and using review processes as teaching moments rather than gatekeeping exercises.
• Incident Leadership & Problem‑Solving: Lead the team through production incidents, post‑mortems, and challenging technical problems — modelling a blameless culture while ensuring systemic improvements emerge from failures.
• Career Development & Growth Paths: Create individual development plans for each team member, identify growth opportunities, and actively sponsor their advancement within Flutter — whether toward senior IC roles or their own leadership tracks.
• Psychological Safety & Culture: Foster a team environment where curiosity is rewarded, failure is learnable, and people feel empowered to propose ideas, challenge assumptions, and take calculated technical risks.

Cross‑Functional Collaboration & Delivery

• Product & Stakeholder Partnership: Work closely with product managers and business stakeholders to translate requirements into scalable technical solutions — balancing ambition with delivery realities and keeping the team focused on high‑impact work.
• Data Science & Engineering Partnerships: Collaborate with data scientists and platform engineering teams across Flutter’s organisation to integrate AI solutions into existing infrastructure, ensuring consistency and operational excellence.
• Technical Communication: Articulate complex AI concepts to non‑technical audiences, present architectural decisions to leadership, and translate business needs into technical priorities for your team.

Security, Compliance & Responsible AI

• Security‑First Mindset Leadership: Champion a security‑oriented approach across all AI development — ensuring the pod reasons about prompt injection risks, data leakage vectors, authentication flows, access control, and sensitive data handling from day one.
• Responsible AI Governance: Ensure all platform development aligns with Flutter’s Responsible AI Policy and regulatory compliance requirements — conducting design reviews, establishing testing practices, and creating guardrail frameworks that scale.

TO EXCEL IN THIS ROLE, YOU WILL NEED TO HAVE

Core Requirements

• Education & Experience: Bachelor’s degree in Computer Science, AI, or a related STEM field, with 5+ years of software development experience, of which 2–3+ years focused on AI/ML or agentic systems development.
• Proven Leadership: Demonstrated experience leading or mentoring technical teams (even informally) — showing ability to grow people, establish technical direction, and drive delivery through others.
• Deep Python & Backend Expertise: Advanced Python proficiency with experience in production frameworks (FastAPI, Flask), async patterns, and the ability to architect scalable backend systems.
• LLM Inference Systems Mastery: Hands‑on expertise with AWS Bedrock, LiteLLM , and similar inference platforms — deep understanding of model routing, API design, cost optimization, and deployment patterns at production scale.
• Context Engineering & RAG Expertise: Demonstrated ability to design and evaluate sophisticated context pipelines