Principal AI Systems Engineer

Traction Ag Inc.; Auburn (IN); Hybrid; USD 100,000 - 130,000; Full time

Benefits offered by this job

Health, Dental, Vision, and Life Insurance

401(k) Plans with Company Match

Unlimited Paid Time Off

Paid Holidays

Job summary

Traction Ag Inc. located in Auburn, Indiana, is seeking a Director of Marketing to spearhead their B2B SaaS marketing initiatives. This role includes leading a cross-functional team to develop AI architecture and implement revenue-driving strategies.

The ideal candidate will have a strong background in software engineering, data engineering, and AI systems, with over 7 years of experience. Responsibilities also include developing secure AI workflows to enhance operational efficiency across the company.

Qualifications

• 7+ years in software engineering or platform/infrastructure roles.
• Demonstrated track record in AI-powered retrieval systems.
• Proficiency in Python, Node, Angular, and TypeScript.

Responsibilities

• Lead the development of internal AI architecture and tooling standards.
• Manage AI-powered retrieval and automation workflows.
• Establish governance and AI usage patterns across the organization.

Skills

Software engineering

Data engineering

AI/ML systems design

Proficiency in Python

Proficiency in Node

Proficiency in Angular

Proficiency in TypeScript

API integrations

AI architecture

Excellent communication

Job description

Traction Ag helps farmers simplify the business of farming through cloud-based software that brings together farm financials and operations. We’re hiring a Director of Marketing to lead and scale a modern B2B SaaS marketing engine focused on pipeline growth, brand positioning, and revenue impact.

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