Principal Agentic Platforms Architect

AI in this role

langchainlanggraphcrewaidatabricks

ragllm-integrationai-agents

Our Purpose

Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build 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.

Title and Summary

Principal Agentic Platforms Architect

The AI Center of Excellence is seeking a Principal Agentic Platforms Architect to lead the technical vision, architecture, and hands-on development of Mastercard’s enterprise agentic AI platforms. This role requires deep, demonstrated expertise building and shipping production-grade AI and agentic systems at enterprise scale, combined with the architectural rigor required in a highly regulated and security-conscious environment

Reporting to senior leadership, you will own the end-to-end platform architecture for agentic AI systems, spanning runtime orchestration, governance, model and tool integration, multi-tenancy, production deployment, and operational lifecycle management. This is a hands-on technical leadership role. You will write and review production code, establish engineering standards, and drive the team toward the highest levels of security, resilience, performance, and engineering excellence

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, including multi-agent coordination, A2A communication, memory governance, tool calling, Model Context Protocol, human-in-the-loop workflows, and autonomous decision-making patterns

- Drive model and platform integration strategy, enabling model-agnostic execution across providers while establishing intelligent routing, cost optimization, abstraction layers, and enterprise governance controls

- Architect API-first platform capabilities that allow internal teams and external consumers to build, deploy, govern, and operate AI agents through secure, scalable, language-agnostic interfaces.
Set and enforce technical standards across the platform, including architecture, code quality, security, resilience, performance, compliance, and operational excellence. Provide rigorous technical direction through architecture reviews and code reviews

- Serve as the senior technical authority and advisor for agentic AI architecture, translating complex technical decisions into clear business and executive-level recommendations while mentoring engineers and strengthening the organization’s technical capabilities

- Establish architectural governance and documentation, including system designs, architecture decision records, integration specifications, and engineering standards aligned with Mastercard’s security and technology principles

All About You

- 12+ years of hands-on software and AI engineering experience, with 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 operating with real users, real data, and real business impact

- Deep hands-on coding expertise, particularly in Python and modern AI/ML frameworks. Experience with agentic frameworks such as LangChain, LangGraph, CrewAI, or equivalent is highly valued

- Strong cloud-native architecture expertise across platforms such as AWS, Databricks, and Kubernetes, with a demonstrated ability to design 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, including 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, simplify complex architecture decisions, and build alignment across engineering and business organizations

- Demonstrated technical leadership and mentorship, with a reputation for high standards, intellectual curiosity, resilience, and developing engineers into stronger technical leaders

- Bachelor’s degree in Computer Science, Engineering, or a related field. An advanced degree is preferred but not required with equivalent experience

Corporate Security Responsibility

All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:

Abide by Mastercard’s security policies and practices;
Ensure the confidentiality and integrity of the information being accessed;
Report any suspected information security violation or breach, and
Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.

How we rate this

Principal Agentic Platforms Architect at Mastercard rates 91 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

RAGLLM IntegrationAI AgentsLangChainLangGraphCrewAIDatabricks

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
How have you integrated a large language model into a production application?
How do you decide when an AI agent can act on its own versus asking for approval first?
What's a project where you used LangChain hands-on?
Walk me through how you've used LangGraph in your day-to-day work.

Adapt your resume

List these exact terms on your resume: RAG, LLM Integration, AI Agents, LangChain, and LangGraph. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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