Principal Agentic Platforms Architect
Mastercard1 day ago
Responsibilities
- Own the enterprise agentic AI platform architecture covering runtime environments, orchestration, governance, multi-tenancy, policy enforcement, guardrails, observability, evaluation, and production lifecycle management.
- Lead hands-on architecture and development of scalable agentic systems, including multi-agent coordination, A2A communication, memory governance, tool calling, Model Context Protocol, human-in-the-loop workflows, and autonomous decision-making.
- Drive model and platform integration across multiple providers, including intelligent routing, cost optimization, abstraction layers, and enterprise governance controls.
- Architect secure, scalable, language-agnostic, API-first platform capabilities for building, deploying, governing, and operating AI agents.
- Set technical standards for architecture, code quality, security, resilience, performance, compliance, and operational excellence, and provide direction through architecture and code reviews.
- Advise senior stakeholders, translate complex technical decisions into business recommendations, mentor engineers, and establish architecture documentation and governance.
Requirements
- 12+ years of hands-on software and AI engineering experience, including substantial experience designing, building, and shipping enterprise AI/ML systems into production at scale.
- Proven experience architecting production-grade agentic AI systems, autonomous agents, multi-agent platforms, or AI-powered automation with real users, data, and business impact.
- Deep hands-on coding expertise in Python and modern AI/ML frameworks; experience with LangChain, LangGraph, CrewAI, or equivalent agentic frameworks is highly valued.
- Strong cloud-native architecture expertise across AWS, Databricks, and Kubernetes, including highly available, fault-tolerant, secure, and horizontally scalable systems.
- Expertise in AI governance, trust, and safety, including guardrails, policy engines, behavioral monitoring, evaluation, red teaming, compliance, and enterprise risk controls.
- Experience with enterprise platforms and data architectures, including multi-tenant systems, fine-grained authorization, identity and access management, API-first architectures, data lakehouse patterns, Delta Lake, vector databases, and RAG pipelines.
- Understanding of model-agnostic AI architectures and tool integration, including multi-provider LLM integration, tool calling, orchestration, and abstraction patterns.
- Exceptional technical and executive communication, technical leadership, mentorship, and the ability to influence engineering and business stakeholders.
- Bachelor’s degree in Computer Science, Engineering, or a related field; an advanced degree is preferred but not required with equivalent experience.
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About Mastercard
Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re building a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.