AI Agent Engineer

About the role

Nutanix Revenue Operations is looking for a hands-on AI Systems Engineer to turn sales, partner and customer-success use cases into working AI prototypes in days, then harden the ones that prove their value into production tools. You will join the Revenue Operations AI team and work directly with sellers, sales leaders, deal desk, forecasting, partner operations and IT. You will be measured by working software in users' hands and the time it saves the revenue organization.

Key responsibilities

• Rapidly prototype AI use cases with the AI Product Manager, defining success metrics and shipping a working prototype within days.
• Design and build agents and reusable skills across multiple vendor platforms, packaging recurring workflows as reusable skills, prompts and templates.
• Connect agents to internal systems of record such as CRM, CPQ, billing and bookings, the data warehouse, product telemetry, support cases and knowledge bases, using MCP servers, APIs and connectors.
• Integrate external sources such as intent and enrichment providers, firmographic data, earnings filings and news, and reconcile them with internal account records.

Required profile

• 5+ years in software engineering, data engineering, solutions engineering or a similar hands-on technical role; 2 years building LLM-powered applications or agents.
• At least five AI agents or LLM workflows shipped to real business users on real data, with a clear end-to-end walkthrough.
• Bachelor's degree in Computer Science, Engineering, Data Science or equivalent practical experience.
• Ability to translate a sales or operations problem into a scoped technical build and explain trade-offs to non-technical leaders.

Required skills

• Strong Python and SQL; REST APIs, webhooks, OAuth, JSON and at least one cloud platform (AWS, Azure or GCP).
• Hands-on experience with at least three agentic or skill-building platforms (e.g. Salesforce Agentforce, Microsoft Copilot Studio, Google Gemini Enterprise, Anthropic Claude, OpenAI Agents SDK, LangGraph).
• Connecting AI systems to enterprise data: CRM, data warehouses (Snowflake, Databricks or BigQuery), document stores and third-party APIs, including retrieval design (chunking, embeddings, vector search).
• Prompt and context design, tool calling, and agent security such as prompt injection and over-permissioned tools.
• Preferred: revenue operations or sales operations experience, Salesforce data models and CPQ, MCP server operation, integration platforms (Workato, MuleSoft, n8n, Power Automate), conversation intelligence and enrichment providers, and a lightweight front end (Streamlit, React, Slack or Teams apps).