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AI Automation Engineer

Job in Sacramento, Sacramento County, California, 95828, USA
Listing for: Proofpoint
Full Time position
Listed on 2026-07-21
Job specializations:
  • Software Development
    AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 123200 - 193600 USD Yearly USD 123200.00 193600.00 YEAR
Job Description & How to Apply Below

The Role

We are looking for a high‑performing AI Automation Engineer to join our CS Operations – GTM Automation & AI team. In this role, you will design, build, and continuously evolve AI‑driven applications and tools—chat assistants, workflow automations, and increasingly autonomous agents—that make our post‑sales revenue‑focused teams more effective and efficient.

Partner closely with key stakeholders, end users, AI Integration, Data Engineering, and Enablement to turn ideas into high‑impact AI capabilities.

This individual‑contributor role reports to the leader of the GTM Systems & Automation pillar, which is responsible for the full CS tech stack.

Your day to day AI Chat Assistants & Conversational AI
  • Evolve our role‑based AI chat assistants—trained to answer questions and take actions tailored to specific personas (e.g., CSM, Renewals).
  • Apply advanced prompt engineering techniques to construct the right level of context, ensuring accurate analysis and reliable output.
  • Continuously test, monitor, and refine assistant performance based on usage data and user feedback.
AI‑Driven Workflow Automation
  • Design and build AI‑driven workflow automations using enterprise‑grade orchestration platforms (e.g., Amazon Quick Automate, Unify Apps).
  • Use AI to process data, apply evaluation and decision logic, generate outputs, and trigger downstream actions.
  • Deploy scheduled and trigger‑based workflows, and design human‑in‑the‑loop checkpoints where judgment or approval is required.
AI Architecture & Optimization
  • Continuously optimize AI system performance—faster data retrieval and analysis, breaking workflows into modular AI agent building blocks, and determining what belongs in a natural‑language prompt versus code (e.g., Python).
  • Assess which AI logic should live at which layer—what should be centralized versus decentralized across vendors and clouds—and continuously evaluate the ensemble of AI tooling: LLMs (Anthropic, OpenAI, Amazon Bedrock, etc.) and orchestration platforms (Amazon Quick Automate, Unify Apps, open source).
  • Stay ahead of the rapidly evolving AI landscape and proactively recommend where new models, frameworks, or techniques should be adopted.
AI Integration
  • Partner closely with the AI Integration and Data teams to expand the AI “brain”—extending data access and connectivity via MCP (Model Context Protocol) and connecting to additional datasets in our data warehouse.
  • Integrate and wire together our AI capabilities (chat, headless, workflow) with the broader CS tech stack, including Salesforce, Gainsight/Totango, Clari, Gong, Pendo, Snowflake/Redshift, AWS, and Power BI.
  • Help determine what AI processing we build and own ourselves versus what we leverage from point‑solution vendors.
Agentic AI Applications
  • Build our first truly agentic CS applications—AI that completes work with minimal to no human intervention—starting with our long‑tail and smaller customer segments.
  • Define the human‑in‑the‑loop guardrails, evaluation logic, and monitoring needed to operate agentic systems reliably and safely at scale.
  • Continuously identify new opportunities across the long‑tail customer base where agentic AI can replace manual, repetitive work.
What You Bring to the Team
  • Bachelor’s degree in Business, Computer Science, Engineering, or related field.
  • 8+ years of professional experience, with at least 2+ years focused on AI technologies.
  • Proven experience building AI applications with demonstrated results.
  • Experience in GTM, ideally within post‑sales functions such as Customer Success.
  • Strong command of advanced prompt engineering; understands how to construct context that drives accurate, reliable model output.
  • Experience architecting multi‑agent systems optimized for performance, reliability, and scale.
  • Working knowledge of Python, with the ability to use AI to write code and to determine which components belong in a prompt versus code.
  • Hands‑on experience with enterprise orchestration platforms (e.g., Amazon Quick Automate, Unify Apps) and LLM platforms (Anthropic, OpenAI, Amazon Bedrock), and tools such as OpenAI Codex, Claude CoWork.
  • Familiarity with the broader CS tech stack—Salesforce, Gainsight/Totango, Clari, Gong, Pendo,…
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