Lead Software Architect

About the role

Mastercard Dynamic Yield is seeking a Lead Software Architect to define and own the end‑to‑end architecture of large‑scale, cloud‑native platforms. The role combines strategic technical leadership with hands‑on implementation, focusing on building resilient, secure and future‑ready systems while integrating agentic GenAI capabilities into production.

Key responsibilities

• Design, evolve and maintain the architecture of distributed, cloud‑native platforms built on AWS.
• Act as the technical focal point across multiple engineering teams, driving alignment and consensus without direct authority.
• Partner with Product to shape long‑term roadmaps and translate business goals into system components.
• Define and enforce architectural principles for scalability, resilience, security and operability.
• Lead the design and production deployment of agentic GenAI systems, including orchestration, state management and cost control.
• Establish best practices for API design, versioning, data contracts and event‑driven communication.
• Conduct architectural reviews, evaluate trade‑offs and guide technical decision‑making.
• Collaborate with Engineering, Product and Data Science to accelerate feasibility and delivery of new features.

Required profile

• 10+ years of professional software engineering experience, including significant time in architecture‑focused roles.
• Proven track record designing, operating and scaling large‑scale distributed systems in production.
• Strong hands‑on experience with AWS and cloud‑native technologies.
• Deep understanding of microservices, APIs, event streaming, asynchronous and event‑driven architectures.
• Experience building and running production‑grade AI/GenAI systems with observability and reliability practices.

Required skills

• AWS (core services, IAM, networking, serverless, containers)
• Cloud‑native architecture and design patterns
• Microservices and API design, versioning, data contracts
• Event streaming and asynchronous communication
• Distributed systems scalability and resilience
• Production‑grade GenAI/AI system orchestration and monitoring
• Observability, reliability, safety and cost‑management practices