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Director- Agentic AI Architect & Lead AI Designer

3 years

4 - 9 Lacs

gurgaon

Posted:3 days ago| Platform: GlassDoor logo

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Skills Required

ai support payments transactions technology networks engineering reasoning power data leadership design orchestration routing extraction architecture spark kafka react layers olap analytics logic graphs controls security persistence inference query redaction compliance model sql redis integration software ml reconciliation tokenization opentelemetry datadog risk integrity report

Work Mode

On-site

Job Type

Part Time

Job Description

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

Director- Agentic AI Architect & Lead AI Designer

Director- Agentic AI Architect & Lead AI Designer Enterprise AI Product Engineering Overview We are building next-generation AI platforms that blend agentic AI, LLM-driven workflows, multi-step reasoning, and autonomous operational agents to power large-scale enterprise operations. We are seeking a Director-Level Agentic AI Architect who sits at the intersection of massive-scale data engineering and advanced agentic intelligence. You will architect the data fabric, the real-time reasoning loops, and the distributed infrastructure required to run AI systems at scale—far beyond prompt engineering. This role begins as a hands-on Principal Architect/IC responsible for delivering Phase 0 (PoC) and Phase 1 (MVP). After successful delivery, the role evolves into product engineering leadership, where you will hire and lead a high-impact cross-functional team (AI, Data, Backend, QA) to collaborate with engineering team to scale the platform. Core Responsibilities 1. Architecting the “AI Operating System
  • Design a multi-agent orchestration layer capable of intelligently routing intent across reasoning, extraction, and decision-making layers.
  • Solve for state, memory, and continuity—build long-running agent processes with episodic, semantic, and working memory so complex investigations never lose context.
  • Lead the 0 1 technical execution, owning architecture, design, and implementation from PoC to MVP before the team scales.

2. High-Velocity Data & Streaming Infrastructure
  • Build the Data-for-AI backbone: petabyte-scale data pipelines that feed agents with real-time and historical context.
  • Implement stateful streaming systems (Flink/Spark Streaming/Kafka Streams) enabling agents to react to events (fraud alerts, settlement breaks, operational exceptions) in milliseconds.
  • Architect memory hierarchies:
o Short-term (session/working context)
o Long-term (knowledge base) o Episodic (state history) o Semantic/Vector memory (search + retrieval)
  • Build high-velocity ingestion layers for PDFs, statements, logs, financial records, and large structured transaction datasets.
  • Design a unified data access layer that blends OLTP lookups, OLAP analytics, and vector search into a single access pattern for agents.

3. Agentic AI Engineering & Reasoning Logic
  • Build an ecosystem of specialized agents—extraction agents, QA agents, reasoning agents, diagnostic agents, and decision agents.
  • Define state machines and execution graphs to support multi-step reasoning patterns:
o Chain-of-thought
o Task decomposition o Root-cause investigation
  • Implement grounding, safety, and HITL controls, ensuring agents never hallucinate or generate unsafe operational decisions.

4. FinOps, Performance & Security
  • Build intelligent routing systems that balance accuracy vs. cost across multiple LLMs.
  • Design semantic caching, batching, trace compression, and selective memory persistence to reduce inference cost.
  • Architect systems aligned with financial-grade security:
o Query sandboxing
o PII redaction o Role-based access o Compliance with PCI-DSS and regulated-data controls 5. Leadership & Team Building (Post-MVP)
  • Lead the transition from PoC MVP Production.
  • Hire and manage a 10+ person product engineering team across AI, Data, Backend, and QA.
  • Define engineering culture, operational practices, and the long-term roadmap to scale the platform globally.
Must-Have Technical Skills
  • Agentic Frameworks: Deep expertise in LangGraph (preferred) or LangChain for building multi-agent, stateful orchestration.
  • LLM Engineering:
o Model routing
o RAG-based grounding o Text-to-SQL generation for complex schemas
  • Vector & Memory Systems:
o Vector search (Pinecone / Weaviate / Vespa)
o Semantic caching o Conversation and working memory (Redis / custom memory stacks)
  • Data Engineering:
o Strong SQL skills
o Kafka pipelines & event streaming o Data warehouse integration (Snowflake / BigQuery / Redshift)
  • Security & Guardrails:
o Experience implementing safety layers, guardrails, and restricted tool execution in regulated environments
Architecture & Leadership Experience
  • Years in Software/Data Engineering with 3+ years building AI/ML or LLM-based systems.
  • Proven experience as the Technical Lead/Architect for at least one 0 1 enterprise AI product.
  • Hands-on experience in FinTech / Banking / Payments, ideally involving settlement, disputes, or reconciliation systems.
  • Demonstrated ability to optimize cloud compute, manage LLM token costs, and architect scalable inference workloads.
  • Strong ability to evaluate Build vs. Buy, design abstraction layers, and enforce architectural governance.

Preferred Qualifications
  • Experience with PCI-DSS, PII tokenization, or regulated data frameworks.
  • Hands-on experience with OpenTelemetry, Datadog, or similar tools for LLM/agent observability.
  • Experience designing Human-in-the-Loop systems for high-stakes decision-making.
  • Ability to work with distributed engineering teams in multiple time zones.

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.

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