• Be a primary deployment member for major cluster / data center deployments as well as be able to respond to smaller on-premise deployments, if needed. • Develop your skillset for new data center deployments, deployment planning, and technical AI infrastructure and cluster configurations. • Be responsive to project management, Supply Chain, HPC logical configuration, and Data Center Operations teams during deployment planning and execution. • Collaborate with cross-functional teams to address deployment-related challenges and optimize regional operations. • Interface with vendors to guide the build out of Lambda data center to meet exacting standards. • Understand power/cooling requirements as well as cabling needs required within data center space to support high performance infrastructures to guide data center build outs. • Participate in technical discussions and provide input on data center integration and deployment strategies. • Work closely with cross-functional teams, including Hardware Engineering, Software Engineering, Supply Chain, Customer Experience and Sales, to align data center solutions with business goals. • Be conversant on infrastructure components (compute, storage, networking) used in deployments. Understanding component configuration and integration to guide system deployments, design, and operations. • Collaborate with stakeholders to understand customer requirements and translate them into actionable plans. • Interface with Hardware Engineering and Architecture teams to develop operational standards and requirements for infrastructure deployments. • Follow best practices and guidelines for efficient and consistent data center operations. • Ensure compliance with industry standards and regulations in datacenter safety. • Identify opportunities for process improvement and operational efficiency in data center integration and deployment. • Stay updated with the latest industry trends, technologies, and best practices. • Drive innovation and propose new solutions to enhance data center operations.

• Perform data science or analytics assignments, investigations, and visualization tasks • Deliver results according to Oshkosh and general industry engineering standards and processes • Work in collaboration with data science team, data engineering, software, and other internal engineering groups or business support disciplines as well as external component suppliers • Conduct independent research on new technologies in cloud-based data analytics

• Lead the end-to-end architecture, development, and deployment of AI, including machine learning, GenAI, and Agentic models that are tailored to business use cases • Drive the development of agentic AI systems — including multi-agent orchestration, tool-use, and autonomous task-execution pipelines — to automate complex enterprise workflows • Establish model development standards encompassing data preprocessing, feature engineering, model selection, hyperparameter tuning, evaluation, and documentation • Partner with data engineering teams to ensure robust, scalable, and high-quality data pipelines that support model training and inference • Mature the organization's AIOps (MLOps & LLMOps) capabilities, including CI/CD pipelines for model training, evaluation, deployment, and monitoring • Define and enforce standards for model versioning, experiment tracking, reproducibility, and model registry management • Implement robust model monitoring frameworks to detect performance degradation, data drift, concept drift, and bias in production systems • Serve as an internal AI innovation champion • Build and maintain an enterprise AI roadmap aligned with strategic business objectives • Foster a culture of experimentation through structured ideation programs, hackathons, and proof-of-concept sprints • Partner, support, and execute the organization's AI governance framework

• Lead the end-to-end architecture, development, and deployment of AI solutions • Drive the development of agentic AI systems to automate complex enterprise workflows • Establish model development standards encompassing data preprocessing and feature engineering • Partner with data engineering teams for robust data pipelines • Mature AIOps capabilities, including CI/CD pipelines • Define and enforce standards for model versioning and monitoring • Build and maintain an enterprise AI roadmap aligned with strategic objectives • Foster a culture of experimentation and collaboration • Partner and execute the organization's AI governance framework • Recruit, develop, and retain a high-performing team of AI practitioners • Build strong cross-functional partnerships with technology and business unit leaders • Define and track KPIs and OKRs for the Data Science & AI function