IT Sr. Analyst, AI Engineer

Patrick Industries•Elkhart, IN

About The Position

The IT Sr. Analyst, AI Engineer is responsible for designing, developing, deploying, and supporting enterprise AI solutions, agentic applications, and intelligent integrations across Patrick Industries. As a senior member of the Agile AI Factory, this role owns technical solution design, implementation approaches, engineering quality, and operational performance for assigned initiatives. The position partners closely with Product Managers, Platform Engineers, Data Engineers, and business stakeholders to deliver scalable AI-enabled solutions that drive automation, improve decision-making, and create measurable business value.

Requirements

Bachelor's degree in Computer Science, Data Engineering, Software Engineering, Information Technology, or related field; equivalent experience may be considered
4+ years of software engineering, AI engineering, data engineering, or related experience
Experience designing, developing, and deploying enterprise software applications and integrations
Hands-on experience building and implementing AI, machine learning, generative AI, or agentic solutions
Strong proficiency with Python, SQL, and JavaScript/TypeScript or similar development technologies
Experience with prompt engineering, retrieval-augmented generation, LLM orchestration, and AI development frameworks
Demonstrated system design and software architecture experience
Experience developing APIs, integrations, and distributed application components
Strong understanding of DevOps practices including CI/CD, source control, automated testing, and release management
Experience working in Agile product development environments
Familiarity with Microsoft Azure, Microsoft Fabric, Microsoft Foundry, Azure DevOps, and Dynamics 365 environments
Knowledge of monitoring, observability, and production support practices for enterprise applications
Strong analytical, communication, collaboration, and problem-solving skills

Nice To Haves

Experience with MLOps, model deployment, monitoring, and lifecycle management preferred
Experience supporting manufacturing, operations, supply chain, or industrial business environments preferred
Experience integrating enterprise applications, industrial systems, sensors, or operational technologies preferred
Experience in decentralized, multi-site, or acquisition-driven organizations preferred
Exposure to C#, Rust, or KQL is a plus

Responsibilities

Design, develop, test, and deploy AI-enabled applications, agents, and intelligent automation solutions
Own technical solution design for assigned initiatives, translating business requirements into scalable production-ready applications
Develop agentic workflows, prompt engineering strategies, retrieval-augmented generation solutions, and AI orchestration capabilities
Create reusable frameworks, services, and components that accelerate enterprise AI delivery
Develop technical documentation, design specifications, and engineering standards
Participate in code reviews, design reviews, and architecture discussions to ensure solution quality
Design system components, service boundaries, data flows, integration patterns, and operational workflows for assigned solutions
Evaluate architectural alternatives and make recommendations based on scalability, performance, reliability, and maintainability requirements
Assess technical trade-offs related to cost, latency, compute utilization, and solution complexity
Design solutions that support enterprise growth, operational efficiency, and long-term sustainability
Collaborate with Platform Engineers and Architects to ensure alignment with enterprise standards
Identify and proactively address technical risks, dependencies, and architectural concerns
Build integrations between AI platforms and enterprise applications, including ERP, operational systems, and business applications
Develop APIs, services, and automation frameworks that enable intelligent workflows across the organization
Support implementation of AI capabilities within manufacturing, supply chain, finance, HR, and corporate business processes
Ensure integrations are reliable, secure, scalable, and aligned with enterprise architecture standards
Troubleshoot integration challenges and optimize system performance
Collaborate with technical teams to support end-to-end solution delivery
Build data pipelines and supporting infrastructure required for AI applications and intelligent automation
Partner with Data Engineering teams to ensure data quality, availability, governance, and readiness
Design and implement data structures that support AI model performance and business requirements
Support ingestion, transformation, and preparation of enterprise data for AI use cases
Optimize solutions for performance, scalability, and maintainability
Contribute to enterprise AI and data platform standards and best practices
Develop and maintain testing frameworks, evaluation harnesses, and validation processes for AI solutions
Establish monitoring and observability standards for production AI systems
Track application performance, latency, error rates, model behavior, and output quality
Support deployment, stabilization, issue resolution, and ongoing production operations
Implement regression testing and validation processes when models, prompts, or retrieval layers change
Drive continuous improvement of solution reliability, performance, and supportability
Apply DevOps best practices including CI/CD pipelines, source control, automated testing, and release management
Participate in sprint planning, backlog refinement, stand-ups, reviews, and retrospectives
Collaborate with cross-functional teams to deliver prioritized work efficiently and predictably
Provide effort estimates, identify delivery risks, and communicate project status effectively
Support release planning, deployment readiness, and production rollout activities
Continuously improve engineering practices, development tools, and delivery processes
Provide technical guidance and mentorship to engineers and project teams
Promote engineering best practices and high-quality software development standards
Research emerging AI technologies, frameworks, and development approaches
Evaluate new tools and platforms that may enhance enterprise AI capabilities
Recommend improvements that increase platform scalability, developer productivity, and business value
Support advancement of Patrick Industries' AI maturity and digital transformation initiatives