AI Solutions Architect

What This Person Will Own

AI application development: designing and supporting AI assistants, AI agents, document-processing workflows, prompt/configuration patterns, and agent behavior tied to real business systems. Azure AI and cloud architecture: supporting

Azure AI Foundry/OpenAI-style workloads, Azure application hosting, managed identity, Key Vault, Azure AI Search/RAG, monitoring, and CI/CD delivery.

Agentic workflow automation: building AI-assisted workflows that can read documents, use business tools, retrieve context, summarize results, recommend actions, and hand off to humans when needed.

Document intelligence and OCR workflows: supporting document upload, classification, extraction, confidence scoring, validation, exception handling, and routing back into line-of-business system

API and system integrations: connecting portals, databases, Encompass/LOS-style platforms, Azure DevOps/Asana, third-party APIs, and middleware services. 

Requirements-to-build translation: turning BRDs, meeting notes, acceptance criteria, and stakeholder asks into technical designs, backlog items, and implementation plans.

AI Development Responsibilities:

AI Area Responsibilities Practical Output AI Agents s Assistants Design and enhance assistants that can answer questions, use business context, and guide users through workflows. Agent behavior specs, prompts/configuration, tool definitions, testing notes, user-facing behavior improvements. Azure AI Foundry / Model Platform Support governed AI model usage, model selection, quotas, managed identity/authentication, and production routing. Model routing design, AI resource configuration notes, call/audit logging, spend/cost considerations. RAG / Knowledge Retrieval Implement or support retrieval against internal documents, guidelines, knowledge bases, and business rules. Search indexes, embedding/RAG design, semantic search behavior, source-grounded responses. Document Intelligence Support AI document upload, OCR, extraction, classification, confidence scoring, and validation paths. Document workflows, extraction tests, confidence thresholds, exception/review handling. Tool Orchestration Allow AI agents to safely call business APIs and functions such as pricing engines, eligibility checks, deal context retrieval, or summary generation. Tool schemas, API mappings, role validation, execution logs, error handling. AI Governance s Guardrails Ensure PII/NPPI-aware design, audit trails, role based access, provider boundaries, cost controls, and human-in-the-loop review. AI audit logs, prompt/model version tracking, token/cost logging, data protection controls, approval checkpoints.

Business Application / Integration Responsibilities:

Area Responsibilities Expected Output Solution Architecture Define practical technical approaches for cloud, AI, automation, and integration initiatives. Architecture notes, implementation plans, risk/open item lists. Application Engineering Build or guide web applications, portals, internal tools, APIs, and automation components. Working features, tested workflows, documented changes. Azure / Microsoft Cloud Support App Services, Container Apps, Static Web Apps, Functions, storage, Key Vault, identity, monitoring, and deployments. Secure Azure patterns, environment readiness, deployment support. Workflow Automation Map manual processes and design task, notification, document, scheduling, and status workflows. Workflow maps, trigger/action definitions, exception handling. Project Tooling Support Azure DevOps, Asana, work item synchronization, project hierarchy, and reporting workflows. Backlog mapping, sync design, project tracking instructions. Stakeholder Communication Communicate status, blockers, questions, and tradeoffs to internal leadership and client stakeholders. Clear updates, decision logs, meeting participation, escalated blockers.

Required Technical Profile

External Recruiting Brief Must Have Strongly Preferred / Nice to Have  Azure application/cloud experience  Azure AI Foundry, Azure OpenAI, Azure AI Search, or comparable enterprise AI platform exposure  1 year of AI related experience 4-5 years as a professional technical engineer   REST API integration experience  Agentic AI, function calling/tool use, workflow orchestration, or prompt engineering experience  C#/.NET, TypeScript, Python, or comparable application development background  Document intelligence, OCR, RAG, embeddings, vector search, or semantic search experience  Azure DevOps/GitHub, branch/PR, CI/CD, and deployment discipline  Mortgage/lending, Encompass/LOS, financial services, or regulated data workflow experience  SSO, OAuth, Azure AD/Entra ID, B2C, RBAC, and identity/security concepts  Power Platform, Logic Apps, Azure Functions, Container Apps, App Services, API Management  SQL/data modeling and data integration fundamentals  Knowledge of AI governance: PII handling, audit logs, model/version tracking, and human-in-the-loop review  Ability to read requirements and ask practical clarifying questions  MSP, consulting, or client-service delivery environment