Principal AI Platform Architect

Job Description

Principal AI Platform Architect

Location: New York, NY

Hybrid – 3 days per week in office

Our client is seeking a hands-on Principal AI Platform Architect to lead the design, development, and implementation of enterprise-scale AI platforms and capabilities.

This role combines technical architecture with hands-on engineering, requiring someone who can design scalable AI solutions while remaining actively involved in Python development, platform implementation, and technical problem-solving.

The ideal candidate will have strong experience in AI platform engineering, AWS cloud infrastructure, distributed systems, and agentic AI technologies, with a proven ability to build secure, resilient, and production-ready AI platforms within a large enterprise environment.

Responsibilities

• Design, develop, and implement enterprise-scale AI platforms supporting multiple applications, engineering teams, and business functions.
• Remain hands-on with Python development, contributing to AI platform services, integrations, and technical implementations.
• Lead the technical design and implementation of AI solutions, including model gateways, orchestration frameworks, retrieval systems, agent frameworks, and tool integrations.
• Design and build agentic AI solutions, including multi-agent systems, Agentic RAG, and complex retrieval workflows.
• Develop and optimise cloud-based AI platform capabilities using AWS technologies and services.
• Work closely with engineering teams to build scalable, secure, and production-ready AI solutions.
• Design highly available, fault-tolerant, and resilient distributed systems capable of operating at enterprise scale.
• Implement best practices for RAG, vector search, tool calling, agent orchestration, guardrails, and AI evaluation frameworks.
• Establish and implement technical standards for identity management, access controls, data governance, observability, and platform security.
• Support CI/CD pipelines, automation, and deployment processes across AI platform services.
• Develop monitoring, telemetry, tracing, logging, and alerting capabilities to ensure platform reliability and performance.
• Identify and resolve technical challenges relating to scalability, performance, workload management, and system integration.
• Evaluate emerging AI technologies, frameworks, and tooling through hands-on proof of concepts and technical implementations.
• Conduct architecture and code reviews, provide technical guidance, and mentor engineers while remaining actively involved in development.
• Collaborate with engineering, security, architecture, and business stakeholders to deliver AI platform capabilities aligned with business requirements.

Requirements

• 7+ years of experience in Software Engineering, Platform Engineering, AI Engineering, or Distributed Systems, with strong architecture experience.
• Proven experience designing, building, and delivering enterprise-scale AI platforms supporting multiple applications, teams, and business functions.
• Strong hands-on Python development experience, with the ability to contribute directly to coding, platform development, and technical implementation.
• Strong AWS cloud experience, including designing, developing, and deploying scalable cloud-based platforms and services.
• Hands-on experience with AI architecture and engineering, including LLMs, model gateways, RAG, vector databases, and orchestration frameworks.
• Experience designing and implementing agentic AI solutions, including multi-agent systems, Agentic RAG, and complex retrieval workflows.
• Strong understanding of distributed systems, scalability, high availability, resiliency, and performance optimisation.
• Experience building secure AI platforms, including identity management, access controls, data governance, and enterprise security requirements.
• Knowledge of AI evaluation frameworks, guardrails, observability, monitoring, and production support.
• Experience with CI/CD, automation, and modern software engineering best practices.
• Ability to provide technical leadership, conduct code reviews, and mentor engineering teams while remaining hands-on.
• Strong communication skills and the ability to collaborate with engineering, security, and business stakeholders.