AI Principle Architect
Mastercard (company site, opens in a new tab)PuneOn-siteDirect career pageEst. 60–95 LPA
Posted 15h ago
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Job description
Structured overview of role & requirements
About This Role
- Define and own architecture and technical roadmap for Mastercard's Operational Intelligence Generative AI platform, setting reusable reference architectures and standards across tool calling, multi-agent orchestration, memory, retrieval, and LLMOps.
- Architect enterprise tool-calling layers and multi-agent systems ensuring secure, reliable, scalable integration with internal and external services, including design of self-correcting agents and memory/state management.
- Lead key model, platform, and trade-off decisions; collaborate with engineering, product, security, and business stakeholders; mentor technical teams; build prototypes to de-risk major architectural decisions.
Minimum Requirements
- Bachelor's or Master's degree in Computer Science, AI/ML, Engineering, or equivalent experience.
- Significant experience architecting large-scale distributed systems in production including microservices, event-driven systems, and cloud-native infrastructure.
- Hands-on experience designing and shipping production LLM-powered systems, especially agents with tool/function calling, memory, and retrieval.
- Strong Python (async programming, FastAPI or similar) and SQL skills; deep experience with at least one major cloud platform (AWS preferred) including AI services; solid grounding in security, privacy, and compliance for enterprise systems.
Ideal Candidate Profile
- Experienced backend or distributed systems architect who has addressed enterprise challenges of reliability, integration, security, and scale, transitioning that expertise to generative AI and LLM agent systems.
- Able to influence cross-functional teams without authority, aligning engineering, product, security, and leadership on architecture and technical strategy through clear business trade-offs.
- Comfortable staying hands-on with prototypes, capable of leading architecture decisions for evolving Gen AI platforms in a regulated environment such as financial services.