AI Platform & Services Engineer

Position: Sr. AI Platform and Services Engineer

Duration: 12 Months

Agency: OIT

Job ID: 13759

Client: State of Alabama

Location: Montgomery, AL

Work Mode: Onsite

Interview Type: Virtual or onsite based on location

Position Summary

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.

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.
  
  

Minimum Qualifications

• Bachelor's degree in Computer Science, Information Systems, Engineering, Cybersecurity, or a related technical discipline, or an equivalent combination of education and progressively responsible professional experience.
• 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.
  
  

Preferred Qualifications

• Hands-on experience with at least two of the following ecosystems: OpenAI/ChatGPT and OpenAI APIs; Microsoft Copilot/Copilot Studio/Azure AI services; Google Gemini/Vertex AI/Google Cloud.
• Experience implementing enterprise AI agents, RAG/grounding, knowledge integration, enterprise search, tool/function calling, model routing, or API orchestration patterns.
• Experience with PowerShell, Python, REST APIs, JSON, CI/CD or infrastructure automation, low-code workflow tools, or comparable automation technologies.
• Experience with enterprise IAM, Microsoft Entra ID or comparable identity platforms, OAuth/OIDC/SAML, RBAC, privileged access, service principals/service accounts, secrets, and access reviews.
• Experience implementing security, privacy, data-loss prevention, logging/audit, records/data-handling, responsible AI, or AI governance requirements in technology platforms.
• Experience establishing IT service-management models including incident, request, change, problem, knowledge, service ownership, escalation, monitoring, and operational reporting.
• Experience operating technology in a regulated, government, large-enterprise, or other control-intensive environment.
  
  

Knowledge, Skills, And Abilities

• 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 tradeoffs 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.