Principal Engineer (Onsite) at Tyson Foods, Inc., Corporate Meadow - Springdale, AR

Principal Engineer (Onsite) at Tyson Foods, Inc. in Corporate Meadow - Springdale, AR. Full-time. Est. up to $130k. Apply with swipeandhired.

Management Level:
89
Work Shift:
1ST SHIFT (United States of America) Principal Engineer, Manufacturing Intelligence
The Principal Engineer, Manufacturing Intelligence is a hands-on technical leader and the manufacturing subject matter expert for our enterprise digital manufacturing platform. This role is focused on one central question: how do we turn plant data into intelligence that plant leaders trust enough to run their operations on? It owns what the intelligence layer must do, what it must never be allowed to do, and the evidence that it works, while personally building and proving new capabilities on the plant floor.
This is not an advisory role. You will write code, build data pipelines, and ship agentic AI capabilities alongside our platform engineering team, while bringing deep plant operations expertise into how the platform is designed. You will build and prove new capabilities at a lead manufacturing site, iterating in real time with the people who use the tools every day, then scale what works across our manufacturing network. Success is measured by trusted, deterministic answers, capabilities adopted at the plant level, and measurable operational and financial results, not by features shipped.
Tyson has become a world-class food company that leverages technology; the next decade will require us to become a technology company that produces food. This position sits at the center of that shift, connecting plant operations, data, and AI on a single enterprise platform.
Immediate Focus (First 6–12 Months)

• Deliver automated, multi-plant operations reporting for business unit leadership, built on a shared, governed source of truth.

• Act as metric owner for manufacturing KPIs, defining and signing off on standard formulas and resolving conflicts between competing calculation methods.

• Assist in the governance model development for AI agents (scope, memory, write authority, and independent verification) with the platform team

• Consolidate proven plant-level analytics and AI capabilities into the enterprise platform without disrupting the sites that rely on them today.

• Advance energy intelligence and computer vision capabilities for safety and quality from proof of concept to production.

• Provide the manufacturing perspective on enterprise manufacturing data standards so data models reflect how plants actually operate.

Future Scope & Roadmap

• Build the data path for plant-floor sources that do not flow through the enterprise data platform (PLC and IoT data, energy meters, line vision systems) to enable centerlining and operator-level intelligence at the edge.

• Scale proven intelligence capabilities across the manufacturing network in phases: build and stabilize at a lead site, then onboard network-wide while the next phase is built.

• Advance AI agents from recommendation toward human-approved execution and, over time, narrowly bounded autonomy, built on governed execution controls (audit trails, reconciliation, and financial controls).

• Identify opportunities to replace high-cost, single-purpose vendor solutions with scalable internal builds where the value is clear.

• Explore where plant intelligence and automation converge (AMRs, physical AI, humanoid robotics) as a deliberate part of the role, with room to test ideas that may not work.

Essential Duties and Responsibilities

• Own the manufacturing intelligence layer within our enterprise platform: the metric definitions, the AI agent and governance model, and the proof that both work.

• Define written KPI contracts (formula, grain, source, and aggregation rule) and a single canonical, versioned calculation that every surface reads, including performance boards, operations reports, trends, and AI agents.

• Ensure AI agents call deterministic tools that read the governed source of truth and state clearly when data is missing or stale, so the vast majority of user questions return consistent, deterministic answers.

• Contribute hands-on to the platform codebase: develop and test AI agents, tools, data models, ingestion pipelines, and edge applications, following the team's engineering standards, code review, and CI/CD practices.

• Design operational-domain agents that mirror the plant leadership team (safety, quality and food safety, throughput, yield and vision, reliability and maintenance, people, and cost), each with a defined scope, tool allowlist, bounded memory, and audit trail, with human teaching captured as reviewable candidate knowledge.

• Serve as the manufacturing subject matter expert for the platform engineering team, translating how plants actually run into clear requirements, priorities, and acceptance criteria, and validating outputs against engineering standards.

• Build and prove computer vision and edge solutions (pedestrian and mobile equipment safety, product inspection) and energy intelligence, then work with the platform team to bring them into production at scale.

• Partner with operational excellence leadership so digital tools reinforce our manufacturing operating system and tiered meeting structure rather than compete with them.

• Guide and review the work of plant-side contributors, such as controls engineers, contributing to the platform under established review controls.

• Stay closely tied to operations through regular engagement with plant and business unit leadership and participation in operations reviews, so the platform keeps answering the problems plants actually have.

• Take on targeted operations problem-solving where the payoff is clear and the solution can scale across sites, rather than one-off fixes at individual plants.

• Evaluate vendor proposals for plant-floor intelligence and recommend build-versus-buy decisions with clear cost and value analysis.

• Prepare and deliver clear, data-backed reporting, recommendations, and value stories to operations leadership and the Executive Leadership Team (ELT).

• Perform other assigned job-related duties that align with our organization's vision, mission, and values and fall within your scope of practice.

Scope and Ownership
This role owns what the intelligence layer must do, what it must never be allowed to do, and the evidence that it works. The platform engineering team owns the platform, the engineering standards, and how things are built. Within that split, this role holds decision rights for:

• Formulas and conflict resolution for manufacturing metrics within scope.

• The AI agent and governance model for manufacturing operations.

• Which capabilities are built and proven first at the lead site.

The role reports into the Manufacturing Automation & Data organization and maintains a standing cadence with manufacturing operations leadership.
Qualifications

• Education: 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.

• Experience: 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.

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

Special Skills

• Deep plant operations expertise across safety, quality and food safety, throughput, yield, maintenance, people, and cost, credible enough to earn the trust of plant managers and division leaders from day one.

• 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: PLCs, SCADA/HMI platforms, historians, IoT, and edge computing devices.

• Experience applying computer vision to safety and quality use cases on the plant floor.

• Understanding of ERP systems (SAP preferred) and the controls required when software writes back to systems of record (auditability, reconciliation, SOX).

• Comfort with Git-based workflows, CI/CD, code review, and AI-assisted development tools.

• Lean manufacturing and operating-system experience, including tiered level meetings, centerlining, and OEE.

• Ability to collaborate efficiently in teams of technical and non-technical individuals, and comfort working in a dynamic environment with evolving objectives and iteration with users.

Soft Skills

• Builder's Mindset: Proving ideas with working software on the floor, not slide decks.

• Influence Without Authority: Earning buy-in from plant and division leaders and from engineering peers without direct reporting lines.

• Systems Thinking: Designing for governance, auditability, and scale from the start.

• Strategic Thinking: Aligning intelligence and automation solutions with organizational goals.

• Communication: Articulating value to operators, plant leaders, engineers, and executives alike.

• Problem-Solving: Resolving complex operational, data, and systems challenges.

• Adaptability: Staying current with rapidly evolving AI, automation, and manufacturing technologies.

• Mentorship: Guiding plant-side contributors and team members in technical and professional growth.

• Customer obsession

• Teamwork & Collaboration

• Not eligible for immigration/work authorization support now or in the future
• Not eligible for relocation assistance *

Internal Management & Management Support Applicants:
Automatic notification to your current manager will be initiated upon selection for interview. This applies to all candidates who are level 79 or above.

All professional team members are expected to demonstrate responsible use of approved AI tools, including safeguarding sensitive information, adhering to company policies, and validating AI-generated outputs. Individual Contributors should demonstrate the ability to integrate AI into daily work, improve efficiency through AI-enabled solutions, evaluate outputs critically, and apply professional judgment when using AI. People Leaders should demonstrate the ability to drive responsible AI adoption, enable effective use of approved solutions, identify opportunities to improve team performance through AI, and ensure accountability for the quality and accuracy of AI-supported outcomes.
Relocation Assistance Eligible:
No
Referral Payout Eligible:
No
For Professional Management levels, internal applicants should either upload a resume to their application OR complete their application fully showing current and past work experience in the sections provided.

The successful candidate(s) must be willing and able to perform the physical requirements of the job with or without a reasonable accommodation.

We provide our team members and their families with paid time off; 401(k) plans; affordable health, life, dental, vision and prescription drug benefits; and more.

Tyson is an Equal Opportunity Employer. All qualified applicants will be considered without regard to race, national origin, color, religion, age, genetics, sex, sexual orientation, gender identity, disability or veteran status.

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