AI Engineer
Posted Updated
Description
- 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
- You’re both hands-on and capable of owning the strategic roadmap for AI operations at Kargo
- 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
- 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
- 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
- Prompt libraries, embeddings-based retrieval, or vector databases (Pinecone, Weaviate) and RAG pipelines
- Retool or Streamlit for lightweight internal UIs; ArgoCD or Kubernetes CI/CD experience
- Builder’s Instinct: Ships fast, iterates on real feedback, and knows when to build vs. buy — comfortable defining the problem and executing without a fully specified brief
- Cross-Functional Fluency: Translates between engineers and revenue leadership, earns trust by delivering things that work, and stays close to adoption after deployment
- Bias for Impact: Prioritizes automations that move a real business metric and measures success by adoption and friction reduction — not lines of code
- Ownership & Accountability: Owns the roadmap end-to-end, communicates proactively, flags blockers early, and treats internal users like customers
- Growth Mindset: 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
Skills
- Agentic AI
- AI
- Airtable
- API
- Argo CD
- Atlassian
- Automation
- ChatGPT
- CI/CD
- Claude Code
- Data Engineering
- Data Quality
- Embeddings
- JavaScript
- Kubernetes
- LangGraph
- LLM
- Looker
- MCP
- n8n
- Observability
- OpenAI
- Pinecone
- Prompt Engineering
- Python
- RAG
- SaaS
- Salesforce
- Slack
- Snowflake
- Streamlit
- Vector Databases
- Weaviate
- Zapier