Artificial Intelligence Engineer, Kargo

• You’re both hands-on and capable of owning the strategic roadmap for AI operations at Kargo
• 5–8+ years in systems automation, internal tools, or process/data engineering; hands-on with orchestration platforms such as n8n, LangGraph, Zapier, or Make; strong familiarity with SaaS APIs and system interoperability

Desirable

• As the AI Engineer at Kargo, you will architect, build, and scale AI-powered products and automations for Kargo’s commercial organization
• Operating within the Data & AI team, you are the connective tissue between revenue teams and AI infrastructure — proactively identifying high-value use cases, building intelligent workflows and agentic applications, and deploying trustworthy systems across Salesforce, Snowflake, Slack, and other internal platforms
• Outcomes - What Success Looks Like in 6-12 Months:
• At least 3 high-impact AI automations are live and actively used by commercial teams — measurably reducing manual work or improving data quality across Salesforce, Slack, or Snowflake
• A governance model is in place covering prompt engineering standards, audit trails, and a feedback loop that drives continuous iteration
• Cross-functional stakeholders trust and use the tools you’ve built, and Kargo’s Data & AI leadership has a clear, prioritized AI Ops roadmap that you own and drive
• You’ve established yourself as Kargo’s internal thought leader on applied AI — the person teams come to when they have a problem AI might solve
• Design, build, deploy, and maintain AI-powered automations and agent workflows using modern orchestration frameworks — LangGraph, n8n, OpenAI Responses/Agents tooling, MCP-compatible architectures — with integrations across Salesforce, Slack, Snowflake, Atlassian, Google Workspace, Looker, and Airtable
• Translate business pain points into modular, extensible automation flows that are observable, debuggable, and fault-tolerant; proficient in Python or JavaScript for custom connectors and scripting
• Build production-grade LLM applications — agent workflows, retrieval systems, internal copilots — using ChatGPT Enterprise and related LLM APIs for knowledge surfacing, workflow routing, decision support, and dynamic content generation
• Maintain a governance model for prompt engineering, agent testing, and audit trails; leverage AI-assisted development tools (Claude Code, Cursor, Codex) to accelerate velocity; familiar with evaluation and observability frameworks for LLM applications
• Work cross-functionally with Sales, Client Services, Media Strategy, Marketing, Product, and Ops to discover automation opportunities, prototype quickly, document tooling, and drive self-service adoption
• Own and communicate the AI Ops roadmap to Data & AI leadership — prioritized by business impact, sequenced by feasibility, and grounded in real discovery with commercial teams
• Ships fast, iterates on real feedback, and knows when to build vs. buy — comfortable defining the problem and executing without a fully specified brief
• Translates between engineers and revenue leadership, earns trust by delivering things that work, and stays close to adoption after deployment
• Prioritizes automations that move a real business metric and measures success by adoption and friction reduction — not lines of code
• Owns the roadmap end-to-end, communicates proactively, flags blockers early, and treats internal users like customers
• Stays current on the LLM and AI agent landscape, applies new tooling when it matters, and shares knowledge generously to raise AI fluency across teams