AI Ops Lead

Kargo creates breakthrough cross-screen ad experiences for the world’s leading brands and publishers. Every day, our 600+ employees bring the power of their creativity and diversity to radically raise the bar on what mobile, CTV, AI, social, and eCommerce can do to wow consumers and build businesses. Now 20 years strong, Kargo has offices in NYC, Chicago, Austin, LA,  Dallas, Sydney, Auckland, London and Waterford, Ireland. Humble brag: In 2024, Kargo was recognized as a Best Place to Work by Ad Age and Built In. 

Who We Hire

Success takes all kinds. Diversity describes our workforce. Inclusion defines our culture. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, marital status, age, national origin, protected veteran status, disability or other legally protected status. Individuals with disabilities are provided reasonable accommodation to participate in the job application process, perform essential job functions, and receive other benefits and privileges of employment. 

Title: AI Ops Lead

Job Type: Permanent

Job Location: New York

Salary Range: $140,000 - $180,000

The Opportunity

We’re looking for an AI Ops Lead to architect and manage end-to-end automations that leverage AI tools like Langflow, n8n, and ChatGPT Enterprise. Operating under the Data & AI organization, this role is critical to our operational evolution—focused on eliminating manual handoffs, unifying data across platforms, and enabling dynamic systems that adapt in real-time to Kargo’s fast-paced environment.

You’ll work at the intersection of AI engineering, systems integration, and internal enablement—proactively identifying automation opportunities, building intelligent workflows, and deploying LLM-powered agents that scale our operations across key systems such as Salesforce, Slack, Snowflake, Atlassian (Jira/Confluence), and others.

The Daily To-Do

Design & Automation

• Build, deploy, and maintain AI-enhanced automations using orchestration tools (LangChain, n8n, etc.).
• Develop integrations with key internal systems including:
• Salesforce (e.g., automating lead enrichment, task routing)
• Slack (e.g., interactive bots for data requests, alerting, approvals)
• Snowflake (e.g., MCP server for Cortex agents, semantic layer)
• Atlassian stack (e.g., Jira ticket creation/triage, Confluence updates)
• Google Workspace, Looker, Airtable, and other productivity platforms.
• Translate business pain points into modular, extensible automation flows that are observable, debuggable, and fault-tolerant.

AI Agent Deployment

• Use ChatGPT Enterprise and related LLM APIs to develop internal agents for:
• Knowledge surfacing and summarization
• Workflow routing and decision support
• Dynamic content generation and classification
• Maintain a governance model for prompt engineering, audit trails, and continuous iteration.

Internal Enablement & Strategy

• Work cross-functionally with Sales, Marketing, Product, and Ops teams to discover automation opportunities and prototype solutions quickly.
• Document, publish, and teach AI tooling internally to drive adoption and self-service usage.
• Act as internal thought leader on how Kargo can use lightweight AI agents and workflow automation to reduce costs and increase responsiveness.

Qualifications : 

• 5–8+ years of experience in systems automation, internal tools development, or process engineering.
• Hands-on expertise with orchestration platforms such as n8n, LangChain, Zapier, Make, or similar.
• Proven experience integrating LLMs (OpenAI, Llama, Claude, Gemini) via APIs or enterprise accounts into production workflows.
• Strong familiarity with SaaS APIs and system interoperability— such as Salesforce, Slack, Atlassian, and Google Workspace.
• Proficiency in Python or JavaScript for custom connectors and automation scripting.
• Strong product sense: ability to triage bottlenecks, propose effective automation, and build feedback loops into the tooling.

Nice to Have

• Experience designing or maintaining prompt libraries and embeddings-based knowledge retrieval.
• Exposure to vector databases (e.g., Pinecone, Weaviate), RAG pipelines, or LangChain-type architectures.
• Familiarity with internal tools like Retool, Notion AI, or Streamlit for user interfaces.

Follow Our Lead

• Big Picture:  kargo.com

The Latest:  Instagram (@kargomobile) and LinkedIn (Kargo)