Principal Engineer (Onsite)

Tyson Foods, Inc.•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, what it must not be permitted to do, and how its effectiveness is demonstrated. It combines plant operations expertise with direct software, data-pipeline, and agentic AI development alongside the platform engineering team. The position builds and proves capabilities at a lead manufacturing site, iterates with plant users, and scales successful solutions across the manufacturing network. Success is measured by trusted, deterministic answers, plant-level adoption, and measurable operational and financial results. The initial focus includes automated multi-plant operations reporting based on a shared, governed source of truth; ownership and standardization of manufacturing KPIs; development of AI-agent governance with the platform team; consolidation of proven plant analytics and AI capabilities; and advancing energy intelligence and computer vision for safety and quality from proof of concept to production. The role also contributes manufacturing expertise to enterprise data standards. The longer-term scope includes developing data paths for plant-floor sources such as PLCs, IoT devices, energy meters, and vision systems; scaling proven capabilities in phases; advancing agents toward human-approved execution and narrowly bounded autonomy with appropriate controls; evaluating internal builds as alternatives to costly vendor solutions; and exploring plant intelligence and automation opportunities such as AMRs, physical AI, and humanoid robotics. The role reports into the Manufacturing Automation & Data organization and maintains a standing cadence with manufacturing operations leadership. It holds decision rights for manufacturing metric formulas and conflict resolution within scope, the manufacturing AI-agent governance model, and which capabilities are built and proven first at the lead site. The platform engineering team owns the platform, engineering standards, and implementation methods.

Requirements

Bachelor’s degree in Engineering (Electrical, Mechanical, Industrial, Chemical, or Computer) or Computer Science, or an equivalent combination of plant leadership and hands-on technical experience.
At least 10 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 AI and LLM applications in production, including agentic systems, tool calling, prompt design, evaluation, and guardrails.
Strong SQL and Python skills, with experience in 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-back to systems of record, including auditability, reconciliation, and SOX.
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 user iteration.
Ability and willingness to perform the physical requirements of the job, with or without reasonable accommodation.
Not eligible for immigration or work-authorization support now or in the future.
Not eligible for relocation assistance.

Nice To Haves

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

Responsibilities

Own the manufacturing intelligence layer within the enterprise platform, including metric definitions, the AI-agent governance model, and evidence that both work.
Define written KPI contracts covering formula, grain, source, and aggregation rule, and establish one canonical, versioned calculation used across performance boards, reports, trends, and AI agents.
Ensure AI agents use deterministic tools that access governed data and clearly identify missing or stale data.
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 scope, tool allowlists, bounded memory, audit trails, and reviewable candidate knowledge from human teaching.
Serve as the manufacturing subject matter expert for the platform engineering team, translating plant operations into requirements, priorities, and acceptance criteria and validating outputs against engineering standards.
Build and prove computer vision and edge solutions for safety and product inspection, as well as energy intelligence, and work with the platform team to bring them into production at scale.
Partner with operational excellence leadership to ensure digital tools reinforce the manufacturing operating system and tiered meeting structure.
Guide and review plant-side contributors, such as controls engineers, who contribute to the platform under established review controls.
Engage regularly with plant and business unit leadership and participate in operations reviews to keep platform capabilities aligned with plant needs.
Take on targeted operations problem-solving when the solution has clear value and can scale across sites.
Evaluate plant-floor intelligence vendor proposals and recommend build-versus-buy decisions using cost and value analysis.
Prepare and deliver 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.