Consulting - Managed Services - AI Enabled Solution Architect - Manager

EY•Hartford, DC

36d•$139,100 - $289,700•Hybrid

About The Position

EY is hiring a hands-on Solution Architect for Digital Engineering Managed Services. The role designs, prototypes, and builds modern AI-enabled applications and digital products, with an emphasis on technical depth, engineering judgment, and architecture ownership across build-and-run engagements. The architect spends substantial time designing architectures, writing and reviewing code, building proofs of concept, and guiding engineering teams through complex technical decisions. The position combines hands-on solution architecture with AI-accelerated engineering leadership, non-functional architecture and reliability, build-to-run ownership, and client collaboration. The architect works directly in codebases alongside engineers, validates architectural decisions through prototypes, and ensures designs are secure, maintainable, reliable, and ready for production and long-term managed operations. The role is part of EY’s consulting practice and uses a team-led, leader-enabled hybrid model. For most external, client-serving roles, EY expects people to work together in person 40–60% of the time over the course of an engagement, project, or year. The position is open to candidates anywhere in the country.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related discipline, or equivalent experience.
Typically 8 or more years in engineering and solution architecture roles, with recent hands-on delivery responsibility.
Strong hands-on background in software engineering and modern application architectures.
Deep understanding of cloud-native design, APIs, integration patterns, and data architectures.
Practical experience building AI-enabled features, including prompting, RAG, embeddings, and evaluation.
Comfort working directly in code, infrastructure, and pipelines, not only producing diagrams.
Clear technical communication and a bias toward building to learn.
Demonstrated ability to combine engineering fundamentals with AI-enabled delivery while maintaining trust, quality, and security.

Nice To Haves

Experience with Azure, AWS, or GCP; Azure is preferred.
Experience with containerized platforms.
Exposure to observability stacks, SRE concepts, and incident response.
Experience architecting in regulated environments involving privacy, security, or model-risk considerations.
Relevant cloud, security, or architecture certifications.
Prior consulting or complex product delivery experience is strongly preferred.

Responsibilities

Design end-to-end application architectures across user interfaces, APIs, services, data, AI components, integrations, and runtime platforms.
Prototype and spike solutions—including code, configurations, pipelines, and prompts—to validate architectural decisions early.
Define and evolve reference architectures for AI-enabled products, including RAG patterns, vector search, prompt orchestration, evaluation, and guardrails.
Create Architecture Decision Records, sequence flows, deployment diagrams, and non-functional requirements.
Work directly in codebases with engineers to establish patterns, unblock delivery, and improve technical quality.
Use AI coding and testing tools to accelerate development while maintaining clean architecture, security, and maintainability.
Review AI-generated code, tests, and documentation, using engineering judgment to assess correctness and risk.
Guide teams on prompt design, context management, and reuse of AI accelerators.
Define performance, scalability, resilience, observability, and operability requirements.
Design for reliability using SLOs/SLIs, telemetry, graceful degradation, and incident readiness.
Embed security-by-design, Zero Trust principles, and Responsible AI controls in solution designs.
Prepare solutions for production with runbooks, automation, and clear support handoffs.
Architect solutions that transition cleanly into long-term managed operations.
Design CI/CD pipelines, environment strategies, and release automation with operational simplicity in mind.
Balance delivery speed with sustainability, cost transparency, and service SLAs.
Lead technical workshops and whiteboarding sessions with client architects and engineers.
Explain architectural tradeoffs to technical and semi-technical stakeholders.
Support solutioning and proposals through architecture design, estimates, and delivery approaches.
Follow EY and client policies for data protection, privacy, and confidentiality when using AI tools.
Ensure AI-enabled components are evaluated, monitored, and governed, and maintain human accountability for architecture decisions and production readiness.