GenAI Solutions Architect

Project description

OverviewThe 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

• 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.