IT Analyst, AI Engineer

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So only top-level sections. Within responsibilities we can still have paragraphs or maybe subheadings not required. We'll just list all duties as list items. Qualifications And Skills: The posting has "Qualifications And Skills" then a list of qualifications. We can treat that as Qualifications section (maybe combine with Skills). Since they ask separate Qualifications and Skills sections if content exists. We have both qualifications and skills mixed. Could split: Qualifications (education, experience) and Skills (technical proficiencies). But we need to decide based on content. The list includes: Bachelor's degree..., 2+ years..., Experience designing..., Experience with AI..., Proficiency with Python..., Knowledge of prompt engineering..., Experience with APIs..., Experience with version control..., Familiarity with Agile..., Understanding of software architecture..., Experience with Microsoft Azure..., Familiarity with monitoring..., Experience integrating AI solutions..., Exposure to manufacturing..., Experience in decentralized..., Familiarity with MLOps..., Exposure to C#, Rust, or KQL..., Strong analytical..., problem-solving..., communication..., collaboration skills. We could make Qualifications section for education and years experience, and Skills section for technical proficiencies. But the instruction says prefer a few substantive sections over many thin ones. Could combine into one Qualifications section that includes both education/experience and skills. However they ask to use headings for Qualifications and Skills separately if content exists. Since we have both, we could make two sections: Qualifications (education, experience) and Skills (technical). Let's extract. Qualifications: 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. Skills: 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; Experience with Microsoft Azure, Microsoft Fabric, Microsoft Foundry, Azure DevOps, and Dynamics 365 environments preferred; Familiarity with monitoring, observability, and application performance management concepts; 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; Strong analytical, problem-solving, communication, and collaboration skills. Benefits: "Benefits Include Health, Dental, Vision, Life, Insurances, Paid Vacation, 401K Match, Holidays, Health Club and Tuition Reimbursement". So we have Benefits section. Pay: Not mentioned. Schedule: Not mentioned (though hybrid arrangement mentioned under location? Actually "Work Arrangement: hybrid". That's schedule? It's work arrangement, not schedule. The instruction says include Schedule section only if posting actually states them. It does not give schedule like full-time, part-time, hours. So we omit Schedule. Pay: omitted. Thus sections: About the role, Responsibilities, Qualifications, Skills, Benefits. We need to preserve all substantive information, not invent or summarize away detail. Must not repeat job title as heading. Must not include Job ID etc. Must remove boilerplate like navigation, EEO, etc. We have to remove "Current Team Members Apply Here", "Patrick Industries, a publicly traded company headquartered in Elkhart, Indiana, invites you to join a team...". That's about the company but maybe part of About the role? Actually that is generic company description. Might be considered boilerplate? It's not navigation/legal/EEO but it's generic company overview. The instruction: Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. The company overview may be considered part of the posting but not required? It's substantive? It describes the company. Might be okay to keep in About the role? But About the role should focus on role. The company overview could be considered filler but not explicitly prohibited. However we need to preserve ALL substantive information; do not invent or summarize away detail. The company overview is substantive? It describes the company, but not the role. Might be considered part of the posting but not essential to role. However we could include it in About the role as context. Safer to include it. But we must not repeat job title as heading. The job title is "IT Analyst, AI Engineer". We'll not use that as heading. We need to output only HTML fragment. Let's craft:

About the role

Patrick Industries, a publicly traded company headquartered in Elkhart, Indiana, invites you to join a team of dedicated Team Members who are passionate about delivering high-quality products and exceptional customer service. As a leading solutions provider serving a diverse range of markets across the United States, our commitment to innovation, quality, and sustainability has positioned us as a high growth, diversified and empowered Team of more than 10,000! 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.

maybe) but instruction only mentions h3 for section headings. It didn't forbid other headings, but we could just make a flat list. However to preserve structure, we could include subheadings as or

. But safer to just list all duties as list items, preserving the text. Let's extract each bullet: From AI Solution Development: - 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 Application & Systems Integration: - 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 Data Engineering & AI Enablement: - 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 Solution Quality, Testing & Monitoring: - 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 DevOps & Agile Delivery: - 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 Engineering Design & Technical Contribution: - 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 AI Initiative Execution: - 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 Now Qualifications section: we need to list qualifications (education, experience). Could be a

as well. Qualifications: - 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 Skills section: list skills. Skills: - 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 - Experience with Microsoft Azure, Microsoft Fabric, Microsoft Foundry, Azure DevOps, and Dynamics 365 environments preferred - Familiarity with monitoring, observability, and application performance management concepts - 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 - Strong analytical, problem-solving, communication, and collaboration skills Benefits section: list benefits. Benefits: - Health -