GenAI Solutions Architect - USA (Remote Role)

Overview The GenAI Solutions Architect is the hands-on consulting architect who ensures AI use cases across the bank are designed and connected the right way: aligned to the enterprise AI platform architecture, the approved integration patterns, and the bank’s governance standards. As business and technology teams stand up AI-enabled applications, this role is their design partner, translating platform capabilities and standards into concrete, buildable solution architectures and reviewing designs before they harden. This is a deeply technical role for an architect who still builds: producing reference architectures, integration patterns, and working examples, and pairing with delivery teams to accelerate adoption while preventing rework and governance escapes.

Responsibilities

Solution Architecture & Design Consulting

• Serve as the consulting architect for AI use-case teams across the bank: shape solution designs, data flows, model access patterns, and integration approaches aligned to the enterprise AI platform and gateway architecture.

• Produce and maintain reference architectures, design patterns, and working examples for common use-case shapes (RAG applications, document processing, workflow automation, agent-based patterns).

• Review solution designs against platform standards and governance requirements before build; document findings and drive remediation with delivery teams.

• Advise on model selection, prompt/context architecture, retrieval design, and oversight/guardrail patterns appropriate to each use case’s risk tier.

Platform Alignment & Standards Adoption

• Ensure all designs route model access through the governed enterprise gateway with correct entitlements, quotas, and logging; prevent parallel or ungoverned access paths.

• Translate governance standards into architecture requirements delivery teams can implement, and feed practical gaps back into the standards process.

• Partner with platform engineering on the evolution of platform capabilities based on real use-case demand.

• Document network, identity, data-classification, and environment-separation considerations for solution designs.

• Partner with Cybersecurity architecture on AI threat modeling, prompt-injection risk, and adversarial-testing requirements for solution designs.

Enablement & Capability Transfer

• Pair with application teams that lack AI delivery experience; provide hands-on design and build guidance through their first implementations.

• Create and deliver architecture enablement materials, design guides, and pattern documentation in the bank’s repositories.

• Support solution reviews in governance forums with clear, evidence-based architecture assessments.

• Transfer patterns, documentation, and working knowledge to bank FTEs throughout the engagement so capability persists post-contract.

Skills

Must Have

• Minimum of 7 years’ experience in solution architecture, application architecture, or senior engineering roles, including hands-on delivery of cloud-native applications.

• Hands-on experience architecting and delivering GenAI/LLM-based solutions: model integration, RAG pipelines, prompt/context engineering, and agent or workflow patterns.

• Strong Azure experience: Azure OpenAI/AI services, API Management, Entra ID, Key Vault, networking and landing-zone concepts, and environment separation.

• Demonstrated experience producing reference architectures, integration patterns, and design documentation adopted by multiple teams.

• Experience designing within security, risk, and compliance constraints in a regulated environment.

• Strong consulting skills: stakeholder communication, design facilitation, and the ability to influence without authority.

Nice to Have / Preferred Qualifications

• Financial services experience and familiarity with banking SDLC governance (design gates, architecture review, permit-to-build/operate models).

• Experience with AI gateway/governance patterns: model allowlisting, entitlement-based access, usage controls, audit logging.

• Experience with vector stores, retrieval services, evaluation harnesses, and MCP/tool-integration patterns.

• Experience mentoring teams new to AI delivery.