Artificial Intelligence ("AI") Architect

Advanced Systems Design is seeking an Artificial Intelligence ("AI") Architect for our client located in Montgomery, AL.

This position is onsite in Montgomery, AL, and requires in-person availability starting day 1.

Job Overview

The Senior AI Platform & Services Engineer is a senior hands-on technical position responsible for engineering, administering, integrating, operating, and continuously improving enterprise AI platforms and services. The position provides technical leadership across a multi-platform AI environment initially centered on OpenAI/ChatGPT, Microsoft Copilot, and Google Gemini. The engineer establishes repeatable technical patterns, operational standards, integrations, access controls, monitoring, service-management processes, and governance implementation so AI capabilities can be operated securely and reliably as managed enterprise services. This role is expected to remain hands-on and is not primarily a consulting, policy-only, or custom model-research position.

Ideal Candidate Profile: An experienced enterprise platform engineer who can work deeply in the backend while also designing the service structure around AI. The ideal candidate can engineer integrations and controls, own complex troubleshooting, operationalize governance requirements, establish standards, and mentor less-experienced AI platform resources.

Primary

Responsibilities

• Engineer, administer, and continuously improve enterprise AI platforms and the supporting technical services required to operate them at scale.
• Serve as a hands-on technical subject-matter resource across OpenAI/ChatGPT, Microsoft Copilot, Google Gemini, and related enterprise AI technologies.
• Design and implement technical patterns for APIs, connectors, agents, orchestration, knowledge sources, retrieval- augmented generation (RAG), automation, and integrations with enterprise systems.
• Establish platform administration, environment management, access control, configuration, service onboarding, change/release, and lifecycle-management standards.
• Design and implement identity patterns including SSO, RBAC, privileged access, service identities, scopes/permissions access reviews, secrets, and least-privilege controls as applicable.
• Translate approved governance, security, privacy, legal, compliance, records, and data-management requirements into enforceable technical configurations and operating controls.
• Evaluate data flows, model/provider interactions, knowledge sources, connectors, and integrations to identify technical risks, dependencies, logging requirements, and control points.
• Establish monitoring, logging, alerting, auditability, usage reporting, cost/consumption visibility, licensing oversight, and operational performance metrics for AI services.
• Own or lead troubleshooting of complex platform, integration, authentication, authorization, data-access, agent, performance, and service-availability issues.
• Evaluate new AI products, models, platform capabilities, agents, and features and define technical testing, pilot, release, and support-readiness requirements.
• Develop and maintain technical architecture documentation, standards, configuration baselines, runbooks, support models, knowledge articles, and operational procedures.
• Define escalation paths and support boundaries for AI-related incidents and service requests and coordinate with vendors and internal technical teams as needed.
• Identify and implement automation opportunities that reduce manual administration and improve consistency, reliability, observability, and governance.
• Provide technical mentoring and guidance to AI Platform Engineers and other support resources while maintaining hands-on ownership of critical engineering work.
• Partner with cybersecurity, cloud/infrastructure, identity, application, data, architecture, service-management, procurement, legal/compliance, and business teams on AI service delivery.

Required

Qualifications

• Approximately 4 - 7+ years of professional experience in cloud engineering, systems engineering, platform engineering, DevOps, enterprise application administration, automation, security engineering, or comparable technical disciplines.
• Approximately 2+ years of meaningful experience with AI, intelligent automation, machine learning platforms, generative AI, or closely related enterprise technologies is preferred; equivalent depth demonstrated through hands-on delivery may be considered.
• Demonstrated experience engineering or operating complex enterprise cloud/SaaS platforms, integrations, identity controls, APIs, and support processes.
• Strong troubleshooting capability and experience leading technical resolution of multi-system issues involving applications, cloud services, identity, permissions, APIs, or data access.
• Ability to translate architectural, security, privacy, governance, or compliance requirements into practical technical designs and operational controls.
• Strong systems-thinking skills and the ability to understand how AI platforms interact with identity, data, applications, networks, cloud services, security controls, and operational processes.
• Ability to design practical service structures around rapidly changing technology without overengineering or losing operational supportability.
• Ability to evaluate trade-offs across platform capability, security, privacy, cost, user experience, maintainability, and compliance requirements.
• Strong technical documentation, standards development, and communication skills for both engineering and governance audiences.
• Ability to mentor engineers, provide technical direction, coordinate across teams, and maintain ownership through implementation and steady-state operations.

Preferred

Qualifications

• Hands-on experience with at least two of the following ecosystems: OpenAI/ChatGPT and OpenAI APIs; Microsof Copilot/Copilot Studio/Azure AI services; Google Gemini/Vertex AI/Google Cloud.
• Experience implem