GenAI Solutions Architect

GenAI Solutions ArchitectLocation: Remote within the USA, or onsite in Buffalo, NY / Wilmington, DE (client preference for candidates near these areas). New hires are required to work onsite at the client's office for the first 2–3 weeks (treated as a business trip; travel expenses covered by the company).Company Overview: Glint Tech Solutions is a women-owned, global IT staffing and recruiting firm serving enterprise clients across the USA and Canada.Project Description: A leading financial services client is seeking a GenAI Solutions Architect to serve as the hands-on consulting architect ensuring AI use cases across the bank are designed and connected the right way — aligned to the enterprise AI platform architecture, approved integration patterns, and 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, while pairing with delivery teams to accelerate adoption and prevent rework and governance escapes.Key ResponsibilitiesServe as the consulting architect for AI use-case teams across the bank, shaping solution designs, data flows, model access patterns, and integration approachesProduce 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 teamsAdvise on model selection, prompt/context architecture, retrieval design, and oversight/guardrail patterns appropriate to each use case's risk tierEnsure all designs route model access through the governed enterprise gateway with correct entitlements, quotas, and logging; prevent parallel or ungoverned access pathsTranslate governance standards into architecture requirements delivery teams can implementPartner with platform engineering on the evolution of platform capabilities based on real use-case demandDocument network, identity, data-classification, and environment-separation considerations for solution designsPartner with Cybersecurity architecture on AI threat modeling, prompt-injection risk, and adversarial-testing requirementsPair with application teams that lack AI delivery experience, providing hands-on design and build guidanceCreate and deliver architecture enablement materials, design guides, and pattern documentationSupport solution reviews in governance forums with clear, evidence-based architecture assessmentsTransfer patterns, documentation, and working knowledge to bank FTEs throughout the engagementMandatory Skills8+ years of experience in solution architecture, application architecture, or senior engineering roles, including hands-on delivery of cloud-native applicationsHands-on experience architecting and delivering GenAI/LLM-based solutions — model integration, RAG pipelines, prompt/context engineering, and agent or workflow patternsStrong Azure experience — Azure OpenAI/AI services, API Management, Entra ID, Key Vault, networking and landing-zone concepts, environment separationDemonstrated experience producing reference architectures, integration patterns, and design documentation adopted by multiple teamsExperience designing within security, risk, and compliance constraints in a regulated environmentStrong consulting skills — stakeholder communication, design facilitation, and the ability to influence without authorityNice-to-Have SkillsFinancial 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 loggingExperience with vector stores, retrieval services, evaluation harnesses, and MCP/tool-integration patternsExperience mentoring teams new to AI delivery