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# AI Engineer

- **Company:** Kargo Inc.
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $140,000.0 - $180,000.0
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, JavaScript (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Automated Storage and Retrieval Systems, Software as a Service, Information Engineering, Cursor, Decision Support Systems, Fault Tolerance, Interoperability, Python (Programming Language), Routing, OpenShift, OpenAI, Salesforce.Com, Systems Integration, Pinecone, Scripting, Prompt Libraries, Power Platform Integration, Retrieval-Augmented Generation, Large Language Models, Claude Code, Snowflake, Zapier, Kubernetes, Slack, Atlassian Tools, Weaviate, Nintex, Dynamic Content, Gsuite, Streamlit Framework, AIOps, Looker Analytics, GPT, Airtable
- **Published:** October 22, 2026
- **Apply:** https://www.juju.com/job/16_50ae21066

## About the Role

Techies who want to build the future. Creatives who want to design it better. Communicators to win business. Collaborators to build it. Data pros who turn numbers into insights. Product builders who turn ideas into innovations. Anyone eager to be on a team that doesn't stop to ask what's next, because they're already building it., * 5-8+ years in systems automation, internal tools, or process/data engineering, with hands-on experience in orchestration platforms such as n8n, LangGraph, Zapier, or Make

 * Proficiency in Python or JavaScript for custom connectors and scripting, with strong familiarity with SaaS APIs and system interoperability
 * Experience building production-grade LLM applications using ChatGPT Enterprise and related LLM APIs, including familiarity with evaluation and observability frameworks
 * Fluency with AI-assisted development tools (Claude Code, Cursor, Codex) to accelerate velocity
 * Proven ability to translate between engineers and revenue leadership, earning trust by delivering things that work and staying close to adoption after deployment
 * A builder's instinct and bias for impact-ships fast, iterates on real feedback, knows when to build vs. buy, and measures success by adoption and friction reduction, not lines of code
 * Nice to have: prompt libraries, embeddings-based retrieval or vector databases (Pinecone, Weaviate), RAG pipelines, Retool or Streamlit for lightweight internal UIs, and ArgoCD or Kubernetes CI/CD experience

In accordance with applicable federal, state, and local pay transparency laws, the anticipated base salary range for this position is listed below. In addition to base salary, this role is eligible for variable compensation - either the Kargo Sales Incentive Plan (sales roles) or an annual discretionary bonus (all other roles). Actual compensation may vary based on factors such as geographic location, work experience, education, and skills.

## Description

* 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
 * Build production-grade LLM applications-agent workflows, retrieval systems, internal copilots-for knowledge surfacing, workflow routing, decision support, and dynamic content generation
 * Translate business pain points into modular, extensible automation flows that are observable, debuggable, and fault-tolerant
 * Maintain a governance model covering prompt engineering standards, agent testing, audit trails, and feedback loops that drive continuous iteration
 * 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
 * Serve as Kargo's internal thought leader on applied AI-staying current on the LLM and agent landscape and sharing knowledge generously to raise AI fluency across teams