Enterprise Architect - Heavy AI/ML Focus

PRI Technology; Jersey City (NJ); Hybrid; USD 180,000 - 230,000; Full time

Benefits offered by this job

Generous benefits package

Job summary

PRI Technology in Jersey City, NJ is seeking an Enterprise Architect with a focus on AI to lead a small team. The role is 3 days onsite and 2 days remote, driving design and governance of AI platform capabilities.

You will guide the architecture across AI models, data pipelines, security and compliance, and integration with enterprise systems, delivering scalable, resilient solutions.

Qualifications

• 10+ years of enterprise architecture experience.
• 5+ years focused on AI/ML platforms and solutions.
• Strong background in large-scale distributed systems.
• Experience with major cloud AI services and platforms.
• Deep understanding of AI security and governance requirements.
• Expert knowledge of AI/ML architectures and platforms.
• Expertise in data architecture and integration patterns.
• Strong technical leadership and communication abilities.
• Experience with LLMs and GenAI technologies.
• Knowledge of MLOps and model lifecycle management.

Responsibilities

• Design and maintain client’s enterprise AI platform architecture.
• Establish technical standards and integration patterns for AI services.
• Lead governance, security, and compliance across AI implementations.
• Define roadmaps and monitor platform scalability and performance.
• Guide teams on technical implementation approaches and reusable components.
• Review and approve AI solution designs and ensure EA standards.

Skills

Enterprise Architecture

AI/ML Platforms

Distributed Systems

Cloud AI Services

AI Security & Governance

AI/ML Architectures

Data Architecture

MLOps

Leadership & Communication

RAG Architectures

Job description

I have a full time role for one of our top tier clients in Jersey City, NJ looking for an Enterprise Architect with AI focus. This role will be 3 days onsite, 2 days remote and lead a small team.

This role does come with a very generous base salary and employee benefits package.

Responsibilities

• Design and maintain Client's enterprise AI platform architecture
• Establish technical standards for AI solution implementation
• Define integration patterns for AI services across Client's technology landscape
• Create and maintain AI reference architectures, including RAG implementations
• Develop model registry and lifecycle management frameworks
• Lead the technical design of AI platform components and services
• Define agent orchestration standards and patterns
• Establish guardrails for AI implementation and deployment
• Create architectural patterns for knowledge base governance
• Guide teams on technical implementation of AI solutions
• Develop technical standards for AI model deployment and management
• Create governance frameworks for AI platform components
• Define security and compliance requirements for AI implementations
• Establish monitoring and observability standards
• Maintain architectural compliance across AI initiatives
• Review and approve AI solution designs
• Ensure alignment with enterprise architecture standards
• Identify opportunities for reusable components
• Guide teams on technical implementation approaches
• Evaluate new AI technologies and platforms
• Drive the evolution of Client's AI platform capabilities
• Identify and evaluate emerging AI technologies
• Define roadmap for platform capabilities and features
• Ensure scalability and performance of AI solutions
• Guide integration with existing enterprise systems

Requirements

• 10+ years of enterprise architecture experience
• 5+ years specifically focused on AI/ML platforms and solutions
• Strong background in large-scale distributed systems
• Experience with major cloud AI services and platforms
• Deep understanding of AI security and governance requirements
• Expert knowledge of AI/ML architectures and platforms
• Strong enterprise architecture capabilities
• Deep understanding of security and compliance requirements
• Experience with cloud-native architectures
• Knowledge of AI model lifecycle management
• Expertise in data architecture and integration patterns
• Strong technical leadership and communication abilities
• Large Language Models and GenAI technologies
• RAG architectures and implementation patterns
• Security and compliance frameworks
• Data architecture and governance
• Model operations and MLOps