Integration Engineer, Senior Legal Tech & AI job in Austin or Ventura (Hybrid)

Senior Legal Tech & AI Integration Engineer Pay Range: $75/hour to $86/hour Job Brief: We are looking for a Senior Integration Engineer to architect the next generation of our Legal Operations ecosystem. This role is at the intersection of Enterprise SaaS and Agentic AI. You will be responsible for moving beyond static data syncing to creating a dynamic, real-time context layer for our Legal AI tools using the Model Context Protocol (MCP).

Your primary focus will be to harvest contextualized data records coming from our systems and applications and integrate into a common platform for legal analysis and insights.

This position reports to our Director, Finance & Legal Systems, and is a critical hire as we modernize our professional services billing lifecycle. Key Initiatives & Projects

• MCP Server Development: Architect and deploy MCP Servers for our core SaaS applications (Salesforce, Ironclad, Gong) to enable AI models to securely query real-time deal data without massive ETL overhead.
• The "Pocket Guide" Automation: Build the infrastructure to pull deal summaries, meeting intent (Gong), and firmographics (ZoomInfo) into a unified context window for Harvey.
• Harvey & Ironclad Agentic Sync: Enable Harvey to not only analyze executed contracts but to "look up" live workflow statuses in Ironclad through standardized MCP connections., * Standardizing AI Context: Implement Model Context Protocol (MCP) clients and servers to solve the "N&timesM" integration problem, ensuring our AI tools have a single "USB-C" style connector to our data.
• Complex Data Orchestration: Map complex schemas from Salesforce (Opportunities/Quotes) and Ironclad (Metadata/Attributes) into structured JSON-RPC formats used by MCP.
• Pipeline Reliability: Ensure high-availability for the data streams feeding the Legal AI stack, managing API rate limits and authentication (OAuth/JWT) across all platforms.
• Technical Stakeholder Management: Collaborate with Legal Ops to define "Context Blueprints"-ensuring the AI receives the right data from Gong transcripts or Salesforce fields to minimize hallucinations.

• Deep Integration Expertise: Advanced knowledge of REST APIs, Webhooks, and Event-Driven Architecture.
• AI Protocol Fluency: Hands-on experience (or deep technical understanding) of the Model Context Protocol (MCP) and its implementation (TypeScript/Python SDKs).
• SaaS Ecosystem Knowledge: * Salesforce: Ability to write SOQL/SOSL and navigate complex custom objects.

• Ironclad: Experience with the Ironclad API and Workflow Designer data structures.
• Gong/ZoomInfo: Experience extracting intent signals and firmographic data via API.

• Modern Dev Stack: Proficiency in TypeScript or Node.js (standard for MCP servers) and experience with Jira for sprint-based delivery.

Technical Stack

• AI/Protocol: Google Gemini for LLM, Agent Space, Claude (Cowork and Code), Model Context Protocol (MCP), Harvey AI.
• SaaS:

• Enterprise tools: Slack, GSuite, Atlassian Suite, Snowflake, Databrics
• Sales tools:, Salesforce (CPQ/Sales Cloud), Ironclad CLM, Gong, ZoomInfo, Zip, Workday
• Legal tools: Harvey, SimpleLegal, Logikcull, Airtable,

• Infrastructure: Node.js/TypeScript, Docker (for MCP server deployment), GitHub.