Principal Engineer (Onsite)

Tyson Shared Services•Springdale, AR

1d•Onsite

About The Position

The Principal Engineer, Manufacturing Intelligence is a hands-on technical leader and manufacturing subject matter expert for Tyson’s enterprise digital manufacturing platform. The role defines what the manufacturing intelligence layer must do and must not do, establishes evidence that it works, and personally builds and proves capabilities on the plant floor. It combines manufacturing operations expertise with software, data, automation, and AI development, partnering with platform engineering while iterating with plant users at a lead site and scaling proven capabilities across the manufacturing network. In the first 6–12 months, the role will deliver automated multi-plant operations reporting based on a shared, governed source of truth; own manufacturing KPI definitions and resolve calculation conflicts; help develop AI-agent governance; consolidate successful plant analytics and AI capabilities into the enterprise platform; advance energy intelligence and computer vision for safety and quality; and contribute the manufacturing perspective to enterprise data standards. The longer-term roadmap includes connecting plant-floor data sources such as PLCs, IoT devices, energy meters, and vision systems; scaling proven capabilities in phases; advancing AI agents toward human-approved execution and narrowly bounded autonomy with appropriate controls; evaluating internal alternatives to costly vendor tools; and exploring plant intelligence and automation, including AMRs, physical AI, and humanoid robotics. The role owns manufacturing metric definitions and conflict resolution, the manufacturing AI-agent governance model, and decisions about which capabilities to build and prove first at the lead site. The platform engineering team owns the platform, engineering standards, and implementation approach. The role reports into the Manufacturing Automation & Data organization and maintains a standing cadence with manufacturing operations leadership. The successful candidate must be willing and able to perform the physical requirements of the job, with or without reasonable accommodation. Immigration/work authorization support and relocation assistance are not available.

Requirements

Bachelor's degree in Electrical, Mechanical, Industrial, Chemical, or Computer Engineering, or Computer Science, or an equivalent combination of plant leadership and hands-on technical experience.
10 or more years of progressive manufacturing operations experience, including plant-level leadership, combined with demonstrated hands-on development of software, data, automation, or AI solutions used in production.
Deep plant operations expertise across safety, quality and food safety, throughput, yield, maintenance, people, and cost.
Hands-on experience building production AI and LLM applications, including agentic systems, tool calling, prompt design, evaluation, and guardrails.
Strong SQL and Python skills and experience with modern cloud data platforms and metric or semantic layer design.
Working knowledge of industrial data and OT/IT integration, including PLCs, SCADA/HMI platforms, historians, IoT, and edge computing devices.
Experience applying computer vision to plant-floor safety and quality use cases.
Understanding of ERP systems and controls for software write-backs to systems of record, including auditability, reconciliation, and SOX; SAP is preferred.
Comfort with Git-based workflows, CI/CD, code review, and AI-assisted development tools.
Lean manufacturing and operating-system experience, including tiered meetings, centerlining, and OEE.
Ability to collaborate with technical and non-technical teams and work in a dynamic environment with evolving objectives and iterative user engagement.
Builder's mindset: prove ideas with working software on the floor rather than slide decks.
Ability to influence plant and division leaders and engineering peers without direct reporting authority.
Systems thinking, including designing for governance, auditability, and scale.
Strategic thinking to align intelligence and automation solutions with organizational goals.
Ability to communicate value to operators, plant leaders, engineers, and executives.
Problem-solving ability for complex operational, data, and systems challenges.
Adaptability to evolving AI, automation, and manufacturing technologies.
Mentorship skills to guide plant-side contributors and team members in technical and professional growth.
Customer obsession, teamwork, and collaboration.
Willingness and ability to perform the physical requirements of the job, with or without reasonable accommodation.
Responsible use of approved AI tools, including protecting sensitive information, following company policies, validating AI-generated outputs, critically evaluating results, and applying professional judgment.

Nice To Haves

Experience in food, protein, or high-speed CPG manufacturing.
Experience with SAP.

Responsibilities

Own the manufacturing intelligence layer, including metric definitions, the AI-agent governance model, and evidence that the capabilities work.
Define written KPI contracts covering formula, grain, source, and aggregation rule, and establish canonical, versioned calculations used across performance boards, reports, trends, and AI agents.
Ensure AI agents use deterministic tools that read governed data and clearly identify missing or stale information.
Contribute hands-on to the platform codebase by developing and testing AI agents, tools, data models, ingestion pipelines, and edge applications in accordance with engineering standards, code review, and CI/CD practices.
Design operational-domain agents for areas including safety, quality and food safety, throughput, yield and vision, reliability and maintenance, people, and cost, with defined scopes, tool allowlists, bounded memory, audit trails, and reviewable candidate knowledge from human teaching.
Serve as the manufacturing subject matter expert for platform engineering, translating plant operations into requirements, priorities, and acceptance criteria, and validating outputs against engineering standards.
Build and prove computer vision and edge solutions for pedestrian and mobile-equipment safety and product inspection, as well as energy intelligence, and partner with the platform team to productionize them at scale.
Partner with operational excellence leadership so digital tools reinforce the manufacturing operating system and tiered meetings.
Guide and review plant-side contributors, including controls engineers, contributing to the platform under established review controls.
Engage regularly with plant and business unit leaders and participate in operations reviews to keep platform development focused on operational needs.
Take on targeted operational problem-solving where solutions have clear value and can scale across sites rather than remaining one-off plant fixes.
Evaluate vendor proposals for plant-floor intelligence and recommend build-versus-buy decisions supported by cost and value analysis.
Prepare and deliver clear, data-backed reporting, recommendations, and value stories to operations leadership and the Executive Leadership Team.
Perform other assigned job-related duties aligned with the organization's vision, mission, and values and within the role's scope of practice.
Deliver automated, multi-plant operations reporting and serve as the owner of manufacturing KPI formulas and conflict resolution.
Help develop governance for AI agents, consolidate proven plant analytics and AI capabilities into the enterprise platform, and advance energy intelligence and computer vision from proof of concept toward production.
Contribute to enterprise manufacturing data standards and build data paths for plant-floor sources that do not flow through the enterprise data platform.
Scale proven intelligence capabilities across the manufacturing network in phases and advance agents toward governed, human-approved execution and narrowly bounded autonomy.
Identify opportunities to replace high-cost, single-purpose vendor solutions with scalable internal builds and explore plant intelligence and automation opportunities.