GenAI Platform Architect

Job Description

Enterprise GenAI Platform Architect

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Location: New York, NY with 3 days in office

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Our client is seeking an Enterprise GenAI Platform Architect to lead the design and evolution of enterprise-scale AI platforms and capabilities. This role will be responsible for defining architecture standards, scalable AI patterns, governance frameworks, and engineering best practices that enable teams to build secure, resilient, and production-ready GenAI solutions.

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The ideal candidate will have deep expertise in enterprise architecture, distributed systems, and generative AI technologies, with experience designing platforms that support multiple products, teams, and business domains within a large financial services environment.

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Responsibilities

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• Define and maintain the enterprise reference architecture for GenAI solutions, including model access, orchestration, retrieval, agent frameworks, tool integration, and runtime controls.

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• Ensure architecture and engineering approaches align with broader platform strategy and enterprise technology standards.

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• Lead end-to-end solution design and delivery for complex AI platform capabilities and services.

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• Partner with engineering, security, architecture, and business stakeholders to translate business needs into scalable technical solutions.

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• Establish architectural standards for identity management, access controls, data governance, observability, resiliency, and human oversight.

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• Define best practices for Retrieval-Augmented Generation (RAG), vector search, tool calling, agent orchestration, guardrails, and evaluation frameworks.

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• Design highly available, fault tolerant, and resilient AI platforms capable of operating at enterprise scale.

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• Develop capacity planning, workload distribution, performance optimization, and scalability strategies across distributed environments.

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• Define service tiering, disaster recovery, and business continuity approaches for AI platforms and services.

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• Evaluate emerging technologies, frameworks, and tooling to improve platform capabilities and engineering efficiency.

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• Drive engineering excellence through architecture reviews, design guidance, code quality standards, and automation practices.

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• Enable and support CI/CD adoption across development teams and platform services.

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• Establish monitoring, telemetry, tracing, logging, and alerting standards to support operational excellence and platform reliability.

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• Conduct technology research, proof-of-concepts, and architecture evaluations to support future platform direction.

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• Provide technical leadership and mentorship to engineering teams implementing enterprise AI solutions.

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Requirements

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• 10+ years of experience in Enterprise Architecture, Platform Architecture, Software Engineering, or Distributed Systems Design.

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• Proven experience designing and operating enterprise-scale platforms that support multiple applications, teams, and business functions.

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• Deep understanding of Generative AI architecture and platform design.

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• Hands-on experience with model gateways, Retrieval-Augmented Generation (RAG), vector databases, orchestration frameworks, tool calling, AI guardrails, and evaluation strategies.

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• Experience designing and operating Agentic RAG architectures.

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• Experience designing and operating multi-agent systems.

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• Experience designing and operating complex retrieval workflows.

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• Experience designing and operating governed AI ecosystems with enterprise controls and oversight.

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• Strong experience designing highly available, scalable, resilient distributed systems.

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• Demonstrated expertise in capacity planning, workload management, performance engineering, and scalability strategies.

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• Experience establishing standards for identity and access management, secure integrations, and data governance.

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• Knowledge of observability frameworks, monitoring, telemetry, auditability, traceability, and operational support models.

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• Experience working within financial services, banking, or other highly regulated environments.

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• Experience building enterprise AI platforms supporting multiple products, business units, and engineering teams.

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• Familiarity with AI model registries, model lineage, governance controls, compliance metadata, and AI benchmarking methodologies.

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• Strong communication and stakeholder management skills with the ability to influence technical and business leaders.

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