Principal AI Systems Engineer

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

• 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

What We Offer

• Mission-driven work that directly supports farmers and rural communities.
• A nimble, passionate team where your ideas have real impact.
• Competitive and cost-effective benefits plans - Health, Dental, Vision, and Life Insurance
• 401(k) Plans with Company Match
• Unlimited Paid Time Off
• Paid Holidays
• A company culture rooted in our values: o Put the Farmer First o Gain Traction as a Team o Think Outside the Silo o Take the Right Next Step o Choose Joy