Lead AI Engineer

Prologis•Chicago, IL

1d•$147,000 - $202,000•Hybrid

About The Position

The Lead AI Engineer builds production AI platform capabilities that transform Prologis building, project, asset, and operational data into reusable solutions for construction, procurement, and operations teams. As a lead individual contributor, this role provides technical leadership through architecture, hands-on implementation, pattern-setting, and ownership of production outcomes rather than people management. The engineer partners with business, technology, security, and operations stakeholders to connect enterprise data, building models, documents, telemetry, digital twins, and workflow systems into governed AI capabilities. The work supports Prologis’s global logistics real estate portfolio of approximately 1.3 billion square feet across 20 countries.

Requirements

6+ years of experience in software engineering, platform engineering, data engineering, applied AI, automation, technology consulting, or a related technical delivery role.
Experience designing, building, deploying, or maintaining AI-assisted workflows, large language model applications, or agentic systems for business use cases.
Experience translating ambiguous workflows, spreadsheets, document repositories, project records, and stakeholder feedback into structured, testable, and governed solutions.
Experience leading discovery, workflow mapping, technical scoping, implementation, rollout, and post-launch iteration with users or domain teams.
Ability to apply context engineering, retrieval-augmented generation, semantic layers, tool or function calling, structured outputs, orchestration, evaluation, and human-in-the-loop design.
Programming or scripting capability in Python, TypeScript, JavaScript, or a comparable language.
Experience integrating APIs, SaaS platforms, data sources, workflow tools, and enterprise systems.
Ability to communicate technical decisions, risks, and outcomes clearly to technical and non-technical stakeholders.

Nice To Haves

Experience with agent frameworks or orchestration patterns such as OpenAI Agents SDK, LangChain, LangGraph, LlamaIndex, MCP, AutoGen, CrewAI, n8n, or comparable platforms.
Experience with Autodesk construction and building platforms, BIM, geospatial systems, reality capture, simulation platforms, or digital twins.
Experience with AWS IoT, AWS Lambda, event-driven or serverless architectures, streaming telemetry, rules engines, or cloud-native operational data platforms.
Experience with building lifecycle systems, operational technology, building management systems, asset management, estimating, bid management, project controls, or field support.
Experience with data platform patterns such as knowledge graphs, entity resolution, ontologies, vector databases, Snowflake, data lakes, semantic layers, or BI and reporting.
Demonstrates willingness and capability to leverage emerging technology, automation, and AI tools to improve efficiency, quality, and speed. Exercises sound judgment, creative thinking, and accountability for outcomes.

Responsibilities

Design and implement production AI platform capabilities, including agents, retrieval-augmented generation, tool calling, workflow orchestration, evaluation, and human review.
Build an AI-native building knowledge platform that links building models, assets, documents, telemetry, and operational context.
Develop multimodal data pipelines that convert BIM, Autodesk, geospatial, document, and operational data into structured, queryable knowledge.
Deliver AI workflows supporting design, construction, commissioning, building operations, sustainment, and reinvestment use cases.
Move priority solutions from prototype through pilot and production with appropriate governance, documentation, evaluation, observability, and operational readiness.
Establish reusable engineering patterns that enable successful pilots to scale into enterprise platform capabilities.
Partner with business, technology, security, and operations teams to define workflows, technical scope, implementation plans, rollout approaches, and post-launch improvements.