IT Analyst, AI Engineer

Patrick Industries•Elkhart, IN

68d

About The Position

The IT Analyst, AI Engineer is responsible for designing, building, testing, deploying, and supporting AI-enabled applications, integrations, and agentic solutions across Patrick Industries. As a hands-on engineering role within the Agile AI Factory, this position develops production-ready AI capabilities that automate business processes, enhance decision-making, and integrate AI technologies with enterprise systems. The AI Engineer partners closely with product managers, platform engineers, data engineers, and business stakeholders to deliver scalable, secure, and reliable AI solutions that support enterprise objectives.

Requirements

Bachelor's degree in Computer Science, Data Engineering, Software Engineering, Information Technology, or related field; equivalent experience may be considered
2+ years of software engineering, data engineering, AI engineering, or related experience
Experience designing, building, testing, and supporting software applications and integrations
Experience with AI, machine learning, generative AI, agent-based systems, or intelligent automation solutions
Proficiency with Python, SQL, and JavaScript/TypeScript or similar development languages
Knowledge of prompt engineering, AI orchestration frameworks, and retrieval-augmented generation concepts
Experience with APIs, system integrations, and enterprise application connectivity
Experience with version control systems, automated testing, and CI/CD practices
Familiarity with Agile development methodologies including Scrum and Kanban
Understanding of software architecture, system design, and engineering best practices
Familiarity with monitoring, observability, and application performance management concepts
Strong analytical, problem-solving, communication, and collaboration skills

Nice To Haves

Experience with Microsoft Azure, Microsoft Fabric, Microsoft Foundry, Azure DevOps, and Dynamics 365 environments preferred
Experience integrating AI solutions with enterprise systems and business processes preferred
Exposure to manufacturing, operations, supply chain, or industrial environments preferred
Experience in decentralized, multi-site, or acquisition-driven organizations preferred
Familiarity with MLOps, model deployment, monitoring, and lifecycle management is a plus
Exposure to C#, Rust, or KQL is a plus

Responsibilities

Design, develop, test, and deploy AI-enabled applications, agents, and automation solutions
Build production-ready software components that support enterprise AI initiatives
Implement AI workflows, agent orchestration, prompt engineering, and retrieval-augmented generation capabilities
Translate business and technical requirements into scalable software solutions
Develop reusable components and integration patterns that accelerate future AI delivery
Create and maintain technical documentation for solutions, integrations, and development standards
Build integrations between AI platforms and enterprise systems, including ERP, business applications, and operational technologies
Develop APIs, services, and integration layers that enable secure and reliable information exchange
Support connectivity between AI solutions, data platforms, and business applications
Troubleshoot integration issues impacting delivery, performance, and production stability
Ensure integrations align with enterprise architecture and security standards
Collaborate with business and technical teams to support end-to-end solution delivery
Develop data pipelines and supporting infrastructure required for AI applications and workflows
Partner with Data Engineering teams to ensure data quality, availability, and readiness
Support ingestion, transformation, and preparation of data for AI and analytics use cases
Design solutions that optimize data accuracy, performance, and scalability
Assist with implementation of enterprise AI and data platform standards
Support AI model utilization through reliable data architecture and engineering practices
Develop and maintain test cases, evaluation criteria, and regression testing processes for AI solutions
Validate AI outputs and ensure solution quality meets business and technical requirements
Implement monitoring and observability standards for AI applications, workflows, and integrations
Monitor latency, performance, reliability, and output quality in production environments
Support issue resolution, defect remediation, and ongoing production support activities
Continuously improve solution stability and operational performance
Apply DevOps best practices including CI/CD, source control, automated testing, and release management
Participate in sprint planning, backlog refinement, stand-ups, reviews, and retrospectives
Collaborate with cross-functional delivery teams to execute prioritized work
Provide effort estimates, status updates, and risk identification for assigned initiatives
Support deployment planning, release execution, and production readiness activities
Follow enterprise governance, security, and responsible AI standards throughout development
Contribute to solution architecture discussions and engineering design decisions within assigned projects
Identify and communicate technical trade-offs related to cost, scalability, performance, reliability, and maintainability
Evaluate technical feasibility and support development of implementation approaches
Escalate architectural concerns and solution risks when appropriate
Recommend improvements to development standards, tools, and engineering practices
Stay current on emerging AI technologies, frameworks, and software engineering techniques
Deliver technology solutions that support enterprise AI priorities and business transformation efforts
Contribute to automation, intelligent workflow, analytics, and AI-enabled business initiatives
Support development of capabilities across operations, supply chain, manufacturing, finance, and corporate functions
Assist with measurement and monitoring of solution adoption and business impact
Collaborate with Product Managers and business stakeholders to ensure delivered solutions achieve intended outcomes