AI & Automation Engineer

Foley Equipment•Kansas City, MO

1d•Hybrid

About The Position

The AI & Automation Engineer is a hands-on technical role responsible for designing, building, deploying, and supporting AI agents, retrieval-augmented generation (RAG) capabilities, and automation solutions on Company-approved AI platforms. The position turns prioritized use cases into secure, supportable production solutions, develops reusable patterns and knowledge layers, and serves as the primary technical builder for the Company's AI and automation programs under the direction of the Senior Manager of Infrastructure and AI. The role requires weekly in-person attendance at a Foley Equipment office, with a partial work-from-home opportunity. Fully remote work is not available. Kansas City or Park City is preferred. The engineer develops AI agents and Power Platform components, integrates solutions with enterprise systems, and moves prototypes into production using environment strategy, solution packaging, application lifecycle management, publishing, and release validation. The role also manages Azure AI Foundry projects and deployments, builds reusable RAG pipelines, configures secure network access and identity, and evaluates agent prompts, tools, and outputs. The position ensures solutions meet security, data protection, and responsible AI requirements. It supports AI intake and review, Microsoft 365 Copilot readiness, production monitoring and third-tier support, solution documentation, technical training, and communication of use-case status, risks, dependencies, and business value to the Senior Manager of Infrastructure and AI and business stakeholders. The position requires a bachelor's degree in a related field, at least three years of relevant IT experience, including one year building and deploying production generative AI solutions, and hands-on experience with Microsoft Copilot Studio, Power Platform, and Azure AI Foundry. Some after-hours work may be needed for urgent issues or production automation failures. Business travel and the ability to drive are required.

Requirements

Bachelor’s degree in computer science, information systems, data analytics, or a related field.
Three years of proven experience in application development, automation, data, or related information technology work.
One year of experience building and deploying production generative AI solutions, such as agents, RAG solutions, or LLM-based automations.
Hands-on experience with Microsoft Copilot Studio, Power Platform, and Azure AI Foundry.
Strong understanding of generative AI, AI agents, RAG, automation, low-code/no-code development, and practical business process improvement.
Working knowledge of Azure networking, Entra ID, managed identities, and RBAC as they apply to AI workloads.
Proficiency in at least one of Python, PowerShell, C#, or TypeScript, as well as REST APIs, JSON, and Git.
Strong knowledge of data privacy, security, access controls, responsible AI practices, and change management.
Ability to diagnose problems across platform boundaries using logs, run histories, and vendor documentation, and explain technical designs in plain language to leaders at all levels.
Well-organized, practical, collaborative, and attentive to detail.
Ability to drive and a valid driver’s license.
Ability and availability to travel for business purposes.
Ability to meet the described physical demands, including regular sitting, seeing, speaking, and hearing; frequent and regular handling or feeling with hands and fingers; recurring visual tasks; constant keyboard work; and driving.

Nice To Haves

Experience with Azure AI Search, Microsoft Fabric, and ERP or CRM integration, such as Dynamics 365 or similar.
Microsoft AI-102, PL-400, or AZ-204 certification.

Responsibilities

Design, build, test, and deploy AI agents in Microsoft Copilot Studio and Foundry, and publish them to approved channels such as Microsoft Teams.
Build Power Platform components, including Power Automate flows, Dataverse data models and security roles, model-driven and canvas apps, AI Builder prompts, and Adaptive Card review experiences in Teams.
Integrate agents and flows with enterprise systems using connectors, REST APIs, Microsoft Graph, Azure Functions, Logic Apps, and MCP tool connections.
Move prototypes into production using environment strategy, solution packaging, ALM, publishing, and release validation; assist with evaluating proposed solution costs.
Write supporting code in Python, PowerShell, C#, or JSON when low-code tooling is insufficient, and use AI-assisted development tools to accelerate delivery and legacy code modernization.
Deploy and manage Azure AI Foundry projects, model deployments, and agents using Company-owned Azure Storage, Azure AI Search, and Cosmos DB.
Build reusable RAG pipelines, including ingestion, chunking, embedding, indexing, and vector and hybrid search.
Configure network isolation and identity for AI workloads, including private endpoints, private DNS, managed identities, and RBAC; troubleshoot connectivity and authorization issues.
Write system prompts and agent instructions, implement tool calling and orchestration, and run evaluations before and after releases.
Build solutions in accordance with Company security and data protection requirements, including least-privilege identities, scoped security roles, Entra ID app registrations, DLP policies, sensitivity-label requirements, and confidentiality obligations.
Verify agent authentication models and ensure agents return only information the requesting user is authorized to access; apply content-safety controls, input and output validation, and human review for consequential actions.
Support AI intake and review through technical assessments, help identify and remediate unsanctioned AI use, and support Microsoft 365 Copilot readiness, including permissions and oversharing remediation.
Monitor solutions for accuracy, performance, usage, security, cost, and ongoing fit with business needs; track licensing, token, credit, and capacity use and recommend cost-control actions.
Provide third-tier support and root-cause analysis for production AI and automation solutions, including occasional after-hours work for urgent issues or production failures.
Maintain solution documentation, including business owner, purpose, architecture, data sources, access model, prompts, testing notes, support owner, change history, and retirement criteria; follow Company change, release, and lifecycle practices.
Deliver demos and technical training on approved AI tools, mentor citizen developers and IT colleagues on approved patterns, and review work for security and supportability before production.
Communicate the status, risks, dependencies, and expected business value of assigned use cases to the Senior Manager of Infrastructure and AI and business stakeholders.