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 What We Are Looking For Required - 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 Success Looks Like Foundation - 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 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: - Put the Farmer First - Gain Traction as a Team - Think Outside the Silo - Take the Right Next Step - Choose Joy