AI Systems Design Architect, Assistant Vice President

State Street

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Quincy, MABoston, MAPrinceton, NJAustin, TX
Salary: $90,000–$157,500 / yr
Posted: 49 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $187k

This role $124k

$74k most similar roles pay here $241k

This role pays less than 90% of similar roles. Most pay $148,606–$224,562 — the shaded band above. At the midpoint, this role pays about $124k versus about $187k for comparable roles.

Based on 240 similar postings.

Employer

About State Street

State Street Corporation is one of the world''s largest custodian banks and asset managers, providing investment servicing, investment management, and investment research to institutional investors. Industry: Financial Services & Asset Custody

State Street currently has 176 open roles on FindRole.

Listed pay typically runs $120,000–$202,500 across 172 roles with salary data.

Most-posted roles

View all roles at State Street

At a glance

TL;DR · AI Systems Design Architect, Assistant Vice President

Role Posting Log in to save

The AI Systems Design Architect - Assistant Vice President leads the end-to-end system design for a major Generative AI platform and shared AI services. This role involves translating business requirements into robust technical architectures, defining integration contracts, and establishing non-functional requirements across the AI stack. The architect will build reference architectures for RAG, model serving, prompt workflows, and agentic AI workflows while ensuring solutions are secure, resilient, and compliant with enterprise standards. Key responsibilities include conducting architecture reviews, evaluating proof-of-concepts, and collaborating with cross-functional teams to ensure production readiness. Required expertise includes Generative AI, LLMs, vector databases, and cloud platforms like AWS, Azure, or Google Cloud. The role demands proficiency in microservices, distributed systems, API architecture, and navigating complex requirements regarding Responsible AI, model governance, and data handling within a large-scale enterprise environment.

Skills

Generative AI LLMs RAG Machine Learning AWS Azure Google Cloud Platform Vector Databases Microservices API Architecture Distributed Systems Event-Driven Architecture DevSecOps System Integration Enterprise Architecture Cloud-Native Services

What you'll do

Own end-to-end system design for the GenAI platform, including model integration, orchestration, APIs, and data flows.
Translate business and product requirements into scalable, secure, and maintainable technical architectures.
Define reference architectures, design standards, integration contracts, and non-functional requirements for the AI platform.
Create reusable architecture patterns for RAG, model serving, prompt workflows, and agentic AI workflows.
Design cloud-based solutions on AWS, Azure, or GCP ensuring scalability, observability, and operational readiness.
Ensure all designs comply with Responsible AI, model governance, data handling, and risk management requirements.
Conduct architecture assessments, technical design reviews, and proof-of-concept evaluations for new AI use cases.
Provide guidance on architectural decisions, trade-offs, and standards adherence across multiple development pods.

What we're looking for

Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field is required; a Master's degree is preferred.
8–14 years of experience in solution, enterprise application, platform, or AI/ML architecture is required.
Candidates must have hands-on experience with Generative AI, LLMs, RAG architectures, vector databases, and ML systems.
Proven experience in cloud architecture and solution design on AWS, Azure, or Google Cloud Platform is required.
Expertise in API architecture, microservices, distributed systems, and event-driven architecture is required.
Candidates must have a strong understanding of cloud-native services including compute, storage, networking, and identity management.
Experience designing for security, resiliency, scalability, observability, and performance is required.
Working knowledge of AI governance, Responsible AI, model risk, compliance, and operational controls is required.