Product Lead

**About Thunes**

Thunes is the Smart Superhighway for money movement around the world. Thunes' proprietary Direct Global Network allows Members to make payments in real-time in over 130 countries and more than 80 currencies.

Thunes' Network connects directly to over 7 billion mobile wallets and bank accounts worldwide, via more than 350 different payment methods, such as GCash, M-Pesa, Airtel, MTN, Orange, JazzCash, Easypaisa, AliPay, WeChat Pay and many more.

Members of Thunes' Direct Global Network include gig economy giants like Uber and Deliveroo, super-apps like Grab and WeChat, MTOs, fintechs, PSPs and banks. Thunes' Direct Global Network differentiates itself through its worldwide reach, in-house Smart Treasury Management Platform and Fortress Compliance Infrastructure, ensuring Members of the Network receive unrivalled speed, control, visibility, protection and cost efficiencies when making real-time payments globally.

**Context of the role**

Thunes is seeking a seasoned, technically fluent **Product Lead - AI & Automation COE** to spearhead our AI-driven transformation across the organization. Reporting to the Head of Product Management, you will define and execute a strategic roadmap that embeds AI and automation into the core of our global cross-border payments platform — driving both revenue growth and operational efficiency.

This role sits at the intersection of product strategy, data science, and engineering execution. You'll collaborate closely across all product squads, engineering, compliance, treasury, and operations to turn complex business needs into scalable, intelligent solutions. From identifying high-impact use cases to launching AI-first features, you'll lead initiatives from 01 and beyond.

Success in this role requires hands-on experience delivering real-world AI products, a deep understanding of large-scale data ecosystems, and the ability to connect technical solutions to business outcomes. You should be comfortable designing for automation, scalability, and regulatory compliance — with a clear sense of how APIs and AI infrastructure can be leveraged to build future-ready financial products.

**Key Responsibilities**

**AI & Transformation Strategy**
- Define Thunes' AI adoption strategy & roadmap from the ground up — assessing departmental readiness across data maturity, cultural openness, and aligning each initiative with clear business priorities
- Identify, prioritise, and categorise AI-first opportunities across departments (e.g., payments, compliance, treasury, customer support) — grouping them into revenue drivers (e.g., personalisation, smart FX routing) and efficiency enablers (e.g., intelligent automation, reconciliation operations)
- Develop internal AI playbooks to identify and prioritise opportunities across products, operations, compliance, treasury, and support — including clear criteria for selecting the right approach, whether LLMs, predictive models, automation, or rules-based logic
- Propose AI sequencing strategy — what to build, buy, or prototype in-house over 6-18 months
- Lead experimentation with emerging agentic AI capabilities (e.g., autonomous agents, self-healing workflows), while developing ROI frameworks that weigh time-to-market, infrastructure cost, and operational impact
- Evaluate and recommend vendors or open-source models that align with Thunes' regulatory and infrastructure context, while partnering with leadership to embed AI goals into company OKRs to drive measurable business impact
- Work with Legal, Risk, and Compliance to ensure AI products meet regulatory and ethical guidelines

**Hands on-Technical Product Leadership**
- Drive a "scalability-first" mindset within the product development process, ensuring that new features and enhancements are architected for future growth and increasing transaction volumes
- Translate high-impact business problems into AI/ML product specifications, user stories, and roadmaps — collaborating with data scientists and PMs to design effective model workflows
- Write detailed functional/tech specs for AI-powered features, collaborating with design and frontend/backend engineers
- Partner with Product,Data & Engineering teams to build scalable intelligence layers — whether via foundational AI capabilities (e.g., LLM orchestration, RAG, inference layers), rules-based systems, ML, or automation — based on what best fits the use case. Focus on pragmatic, high-impact solutions that balance time-to-market, scalability, and agility
- Work with engineering to determine and define infrastructure and deployment requirements for AI systems — whether on-premise, cloud-native, or hybrid — based on the specific use case, while accounting for regulatory requirements, data residency, and cost-efficiency. Ensure solutions are scalable, compliant, and sustainable

**KPI Definition & Performance Measurement**
- Define and track key performance indicators (KPIs) related to platform performance, scalability, and operatio