Principal Agentic Platforms Architect
Mastercard1 day ago
London, United KingdomStaff+
Responsibilities
- Own the enterprise agentic AI platform architecture, including 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 with 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 strategy across 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 and enforce technical standards for architecture, code quality, security, resilience, performance, compliance, and operational excellence through architecture and code reviews.
- Advise senior leaders on agentic AI architecture, translate complex technical decisions into business recommendations, and mentor engineers.
- Establish architectural governance and documentation, including system designs, architecture decision records, integration specifications, and engineering standards.
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 is highly valued.
- Strong cloud-native architecture expertise across AWS, Databricks, and Kubernetes, including highly available, fault-tolerant, secure, and horizontally scalable systems.
- Deep expertise in AI governance, trust, and safety, including guardrails, policy engines, behavioral monitoring, evaluation, red teaming, compliance, and enterprise risk controls.
- Experience building enterprise platforms and data architectures involving multi-tenant systems, fine-grained authorization, identity and access management, API-first architectures, data lakehouse patterns, Delta Lake, vector databases, and RAG pipelines.
- Strong 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 skills, with the ability to influence senior stakeholders and align engineering and business organizations.
- Demonstrated technical leadership and mentorship, with high standards, intellectual curiosity, resilience, and a record of developing technical leaders.
- Bachelor’s degree in Computer Science, Engineering, or a related field; an advanced degree is preferred but not required with equivalent experience.
Benefits
- The role operates in a security-conscious, highly regulated enterprise environment with responsibilities for protecting Mastercard assets, information, and networks.
- Employees must follow security policies, maintain confidentiality and integrity of accessed information, report suspected security violations or breaches, and complete mandatory security training.
Tech Stack
Categories
About Mastercard
Mastercard builds and operates a global payments network used by banks, merchants, fintechs, and governments, offering card processing, real-time payments, tokenization, and fraud/risk services. It generates revenue from transaction processing and assessment/service fees across more than 200 countries and territories. Founded in 1966 and headquartered in Purchase, New York, Mastercard is a public company listed on the NYSE (ticker: MA).