AI Engineering Lead

AI Engineering Lead

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.

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, retrieval-augmented generation systems, and prompt architectures — with understanding of trade-offs and best practices.
• Model Tokenomics & Architectural Trade-offs: Strong ability to reason about token economics, latency budgets, context limits, and capability-to-cost ratios when making architectural decisions — mentoring others to think this way.
• Agentic Framework Proficiency: Deep experience with at least 2–3 agent frameworks (LangChain, LangGraph, Strands, ADK, CrewAI) — understanding of orchestration patterns, tool-use design, multi-agent coordination, and when to use which patterns.
• Guardrails & Responsible AI: Substantial experience with model constraint mechanisms, content filtering, guardrail systems, and responsible AI principles in production environments — able to architect these at scale.
• Security-Oriented Mindset: Strong security fundamentals applied specifically to AI — understanding prompt injection, data exfiltration, model misuse vectors, and standard patterns (OAuth, JWT, least-privilege design).
• AI-Augmented Development Practices: Proficient using AI coding tools (Claude Code, GitHub Copilot, OpenCode) as genuine productivity multipliers — able to validate AI-generated code and teach others to do the same.
• Software Engineering Fundamentals: Expert-level knowledge of SDLC, version control (Git), API design, system architecture, and collaborative engineering at scale.
• Communication & Influence: Exceptional ability to communicate complex technical concepts clearly to both technical and non-technical audiences; skilled at influencing without authority and building consensus.

Highly Desirable Skills

• Full-Stack Experience: Demonstrated ability to own end-to-end problems across the stack — experience with React, TypeScript, modern frontend patterns, backend services, and AI integration — showing where full-stack ownership adds value.
• Advanced Vector Databases & RAG: Hands-on experience designing and operating production RAG systems using vector databases (Pinecone, pgvector, OpenSearch, Weaviate) at scale.
• Model Context Protocol (MCP): Deep understanding of MCP and its role in standardising tool and context integration across modern AI agent systems — ideally with implementation experience.
• Open-Source Contributions: Active contributions to AI/ML or agentic frameworks (LangChain, CrewAI, etc.) — demonstrating commitment to the broader AI community.
• Regulated Environment Experience: Prior experience in gaming, sports betting, financial services, or heavily regulated tech environments where compliance, safety, and auditability are non-negotiable requirements.
• Prior Leadership Success: Track record of successfully growing engineers, building high-performing teams, or leading technical initiatives that shipped material business impact.
• Curiosity & Thought Leadership: Demonstrated appetite for staying at the bleeding edge of AI development — active in community discussions, opinionated about technology choices, comfortable forming and defending positions on where the field is heading.