AI Integration Engineer

iManage•Chicago, IL

1d•$130,000 - $180,000•Hybrid

About The Position

The AI Integration Engineer owns agentic and AI-driven integration development, as well as the development and operational practices needed to run those integrations. The role develops autonomous agents that respond to data, events, or AI models—including large language models and internally developed machine learning models—to carry out tasks independently. It applies a Databricks-first approach and uses Claude where it best fits the desired outcome. The engineer also builds applications that extend enterprise data to the organization, for example with Databricks Apps, and contributes to the ongoing development and management of the Ask iMData MCP connector, which interfaces with Databricks and enterprise data sources and supports go-to-market data and AI use cases. The engineer works with business analysts and stakeholders to understand desired outcomes, underlying processes, and where autonomous agents are appropriate, initially emphasizing go-to-market systems and workflows. The role reports to the Machine Learning Engineering Manager and collaborates with the iManage Process Center of Excellence and Data Governance team to document processes and apply standards for naming, logging, auditing, and change management. The position includes defining agent monitoring methods, improving agent performance using measurable metrics, designing event triggers and orchestration, maintaining technical documentation, and managing integration incidents. The role requires a master's degree or higher in computer science, engineering, or an equivalent field, along with 1–2 years of relevant agentic AI experience. The position requires in-office presence on Tuesdays and Thursdays, with a flexible working policy. The US annual base salary range is $130,000–$180,000, with additional compensation such as bonuses and benefits potentially available.

Requirements

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 work with large language models such as Claude and/or internally developed machine learning models.
Proficiency in a programming language such as Python (preferred), Java, or C#.
Experience using SQL to query and manipulate data.
Experience with 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 to initiate workflows.
Experience building monitoring, alerting, and incident-response practices for automated systems, including SLAs, on-call, and runbooks.
Excellent analytical and problem-solving skills, with attention to detail.
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 while managing priorities and dependencies.
Excellent written, oral, and interpersonal communication skills, including the ability to communicate technical information to non-technical audiences.

Nice To Haves

Experience contributing to or maintaining an MCP connector in a production environment.
Experience building skills or tools for AI agents to fulfill 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.

Responsibilities

Develop autonomous agents that respond to data, events, or AI model signals to perform tasks independently.
Develop agentic AI with a Databricks-first mindset, using Claude where it best serves the desired outcomes.
Build 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 Databricks and enterprise data.
Collaborate with business analysts and stakeholders to understand business outcomes and processes, and determine where autonomous agents are appropriate, initially emphasizing go-to-market systems and workflows.
Work with the iManage Process Center of Excellence to document and maintain processes performed by agents and processes for monitoring those agents.
Define and develop methods for monitoring agent operations, including completion, data flow, timing, failure and quality alerts, API errors, SLAs, and dependencies.
Iterate on agents to improve performance using metrics such as safety, tool-selection accuracy, and task completion rate.
Design and maintain event architecture triggers that launch and orchestrate agents.
Work with Data Governance to define and apply naming, logging, and auditing standards for agent operations.
Apply agent and integration change-management standards defined by the Data Governance team.
Prepare and maintain technical documentation of agents and integration operations, including model dependencies, activation triggers, workflows, and data sources.
Manage integration incidents, including triage, escalation, severity, runbooks, on-call, and vendor escalation.

Benefits

Annual performance-based bonus.
Health, vision, dental, and life insurance.
401(k) retirement savings plan with company match up to 4%.
HealthJoy healthcare concierge service.
Enhanced parental leave: 20 weeks of 100% paid primary leave and 10 weeks of 100% paid secondary leave.
Flexible time off policy.
Multiple company wellness days.
Free access to the Healthy Minds app for mindfulness and meditation.
Unlimited access to LinkedIn Learning courses and interactive Microsoft courses and training.
Flexible work hours.
Modern open-plan workspace with gaming area, free snacks and drinks, and regular social events.
Internal development framework for career growth.