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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal AI Systems Engineer - **Company:** Traction Ag, Inc. - **Location:** Auburn, IN, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Automated Storage and Retrieval Systems, JIRA, Audit Trail, Software as a Service, Customer Data Management, Information Engineering, Identity and Access Management, Python (Programming Language), Knowledge-Based Systems, Machine Learning, Automation of Marketing, Node.Js, Open Source Technology, Search Technologies, Software Engineering, TypeScript, Workflow Management Systems, AI Infrastructure, Data Logging, Data Processing, Multi-Agent Systems, Prompt Engineering, IT Architecture, AngularJS, Slack, Atlassian Tools, Figma, Machine Learning Operations, Gsuite, Hubspot, Webhooks, Software Version Control, Data Pipelines, Automation Anywhere - **Published:** August 9, 2026 - **Apply:** https://www.wayup.com/i-j-Principal-AI-Systems-Engineer-Traction-Ag-Inc-012862697280245/ ## About the Role + 7+ years in software engineering, data engineering, or platform/infrastructure roles, with at least 2 years focused on AI/ML systems or AI-powered tooling + Demonstrated track record designing and implementing AI-powered retrieval systems, knowledge architectures, and workflow orchestration patterns in production environments. + Proficiency in Python, Node, Angular, and TypeScript; comfortable working across the stack from data pipelines to lightweight front-end interfaces + Proven ability to build integrations across SaaS tools using APIs, webhooks, and automation platforms + Strong understanding of context engineering: designing retrieval strategies, memory systems, and information architectures that make AI outputs reliable and high-quality + Excellent communication: you can translate between technical architecture and business outcomes, and you can teach complex concepts to non-technical colleagues + Comfortable operating autonomously, prioritizing ambiguous problems, and making pragmatic technical tradeoffs. Nice to Have + Familiarity with structured operating systems for scaling companies + Background in ag-tech, fintech, or B2B SaaS + Experience building internal developer platforms, plugin systems, or self-service tooling for non-engineers + Contributions to open-source AI tooling or a portfolio of internal tools you have built and shipped + Experience with our stack: Atlassian, Notion (including the API), HubSpot, Slack, Jira, Figma, Google Workspace, Canva ## Description Traction Ag helps farmers simplify the business of farming through cloud-based software that brings together farm financials and operations. In this role, you will operate as a cross-functional technical leader partnering closely with the COO and engineering leadership. You will help define the company's AI architecture, tooling standards, and governance practices. Core Priorities 1. Build a secure internal AI data and retrieval layer 2. Establish governance and safe AI usage patterns 3. Ship high-leverage internal workflows and automations 4. Enable responsible AI adoption across the company 5. Create scalable foundations for future agentic systems What the role is not: + An AI research role + A pure ML modeling role + A prompt engineering role + A people management role + A speculative innovation lab What You Will Build The Operating Layer Our internal AI operating layer. A secure internal AI layer that connects company knowledge systems and makes institutional context searchable, usable, and operational. + Building AI-powered retrieval and synthesis workflows across Slack, CRM, Google, docs, project management, and meeting transcripts so teams can access institutional knowledge and historical context in seconds + Creating scalable systems for meeting capture, decision logging, onboarding, SOP generation, and cross-functional communication + Implementing RAG pipelines, vector search, embeddings, and AI orchestration frameworks that power the entire internal AI toolkit + Reducing knowledge silos, duplicated work, and dependency on tribal knowledge by making information flow to where it is needed, when it is needed The Internal AI Workflow Platform A centralized library of reusable AI-powered workflows, automations, and internal tools employees can safely use without exposing sensitive company or customer data. + Curated, tested AI workflows for each department that non-technical team members can invoke without prompt engineering from scratch + Version control, access governance, and audit trails so the organization can scale AI usage without sacrificing security or consistency + A framework that lets team members go from idea to prototype to production-ready workflow, with guardrails that keep outputs safe and on-brand Operational Intelligence + Automations and agents that transform raw information into actionable insights, summaries, tasks, and operational reporting + Tools that make operational metrics, goal tracking, and leadership reporting more accessible, more actionable, and harder to ignore + Governance, security, and data quality standards for every internal AI system Security & Governance + Define safe AI usage standards across the organization + Establish data handling and model access policies aligned with security requirements + Evaluate AI vendors, infrastructure, and deployment patterns for security and scalability + Design human-in-the-loop workflows, auditability, and operational safeguards + Ensure customer financial data is protected across all AI systems, + Initial secure AI retrieval architecture is operational against at least one core company data source + Foundational AI infrastructure, governance standards, and approved tooling patterns are established + At least two vetted internal AI workflows are published and actively used Quick Wins - First 90 Days + Three to five automations are shipped and saving measurable time across multiple departments + At least one cross-functional AI workflow is operational and adopted by non-technical teams + A prioritized six-month roadmap for AI infrastructure, workflow automation, and governance is delivered to leadership Organizational Trust + You have established strong working relationships across department leadership + The organization trusts the systems, guardrails, and architectural direction being established + The company has begun moving from fragmented AI experimentation toward secure, production-oriented AI adoption ## Related Videos - [Agentic employees in world's most downloaded FinTech app](https://www.wearedevelopers.com/videos/100123-agentic-employees-in-world-s-most-downloaded-fintech-app) - [Collaboration Quantified: Lessons from Open Source Developer Networks](https://www.wearedevelopers.com/videos/1422-collaboration-quantified-lessons-from-open-source-developer-networks) - [Boost Productivity with AI: Figma & Playwright MCP Workflows - Aris Markogiannakis](https://www.wearedevelopers.com/videos/1768-boost-productivity-with-ai-figma-playwright-mcp-workflows-aris-markogiannakis) - [Stack Overflow: Community and AI](https://www.wearedevelopers.com/videos/600-stack-overflow-community-and-ai) - [Developer Experience in the Age of AI](https://www.wearedevelopers.com/videos/1118-developer-experience-in-the-age-of-ai) - [The AI-Native Engineering Org: What’s Real, What’s Hype, What’s Next](https://www.wearedevelopers.com/videos/100004-the-ai-native-engineering-org-what-s-real-what-s-hype-what-s-next) ## Related Articles - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [What is Software Engineering in the Age of AI?](https://www.wearedevelopers.com/magazine/640-what-is-software-engineering-in-the-age-of-ai) - [The State of WebDev AI 2025 Results: What Can We Learn?](https://www.wearedevelopers.com/magazine/581-the-state-of-webdev-ai-2025-results-what-can-we-learn)