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iManage

Document management and AI-driven legal workflows

AI Integration Engineer

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeChicago, IL, USA$130k - $180k /yr

About the Job

Requirements

  • A master's degree or higher in computer science, engineering, or an equivalent field.
  • 1–2 years of experience developing autonomous agents or agentic AI solutions, including working with large language models such as Claude and/or internally developed machine learning models.
  • Proficiency in programming languages such as Python, Java, or C#; Python is preferred.
  • Experience using SQL to query and manipulate data.
  • Experience using the Databricks platform, including building and deploying Databricks Apps.
  • Familiarity with the Model Context Protocol (MCP) and connecting AI tools to enterprise data.
  • Experience designing event-driven or trigger-based architecture for initiating workflows.
  • Experience building monitoring, alerting, and incident-response practices for automated systems, including service-level agreements, on-call, and runbooks.
  • Experience applying naming, logging, auditing, and change-management standards defined by other teams, such as data governance.
  • Ability to work independently to execute project requirements, with attention to priorities and dependencies.

Responsibilities

  • Develop autonomous agents based on data, events, or AI models, including large language models and internally developed machine learning models, that respond to triggers and signals to autonomously perform tasks.
  • Develop agentic AI with a Databricks-first mindset, using Claude where best suited to achieve the desired outcomes.
  • Develop applications that extend enterprise data to the organization using appropriate tools, such as Databricks Apps.
  • Contribute to the ongoing development and management of the MCP server that interfaces with enterprise data and Databricks.
  • Collaborate with business analysts and stakeholders to understand desired outcomes and supporting processes, and evaluate where autonomous agents are appropriate, initially emphasizing go-to-market systems and workflows.
  • Collaborate with the iManage Process Center of Excellence to ensure processes performed by autonomous agents and processes for monitoring those agents are documented and maintained.
  • Define and develop methods for monitoring agent operations, including completion, data flow and timing, failure, quality and API-error alerts, service-level agreement tracking, and dependency documentation.
  • Iterate on agents to improve performance measured by metrics such as safety, tool-selection accuracy, and task-completion rate.
  • Design and maintain event-architecture triggers that launch and orchestrate agents.
  • Collaborate with Data Governance to define and apply naming-convention standards.
  • Collaborate with Data Governance to define and apply standards for logging and auditing agent operations.
  • Apply change-management standards for agents and integrations as defined by Data Governance.
  • Prepare and maintain technical documentation of agents and integration operations, including model dependencies, activation triggers, workflows, and data sources.
  • Manage incidents for integrations, including triage, escalation, severity, runbooks, on-call, and vendor escalation.

Desired Qualifications

  • Experience contributing to or maintaining an MCP connector in a production environment.
  • Experience building skills or tools for AI agents to deliver targeted business requests.
  • Understanding of data lake or lakehouse technologies.
  • Working knowledge of go-to-market concepts and systems such as Salesforce or Marketo.
  • Experience supporting go-to-market use cases through data or AI connectors such as iMData.

About the Company

iManage

Document management and AI-driven legal workflows

iManage provides work product management software for law firms and corporate legal departments, combining document management, email management, and AI-driven insights. The platform organizes and indexes documents and emails, uses AI to surface relevant information, automates routine tasks, and offers secure, centralized handling of data in a subscription-based service. It differentiates itself with a strong legal focus, AI features tailored to legal workflows, and a partner ecosystem that supports deployment and ongoing success. Its main goal is to help legal professionals work more efficiently and securely, with less administrative burden so they can focus on delivering legal services.

Company size
1,001-5,000
Company stage
Not listed
Total funding
Not listed
Headquarters
London, United Kingdom
Founded
2015

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