AI Product Engineer

This is a fully remote position, open to applicants in Illinois.

📋 Description

• You are a solution developer who also acts as an engineer, constructing the infrastructure that ensures AI solutions are reliable, scalable, and easy to maintain.

• Your responsibilities will include overseeing the engineering foundation beneath our AI-driven workflow tools: this involves extraction scripts, validation frameworks, output schemas, integration connectors, and quality harnesses that transform a capable AI model into a trustworthy production tool.

• You will establish engineering standards, make architectural choices, and serve as the go-to person for resolving unexplainable pipeline issues.

• You will gain an in-depth understanding of the information landscape in construction and real estate project delivery, including data availability, storage locations, formats, and prerequisites for AI models to produce useful outcomes.

• You will design structured output contracts that dictate the deliverables of AI solutions and create validation logic to enforce these contracts.

• In instances where a solution generates unexpected results or fails silently on atypical documents, you will take ownership of the detection and recovery processes.

• You will define the criteria for production readiness before the development process begins, test solutions against various real-world document sets, and uphold quality standards as the underlying models and input data evolve.

• You will integrate AI solutions with JLL's enterprise environment using REST APIs, Microsoft Graph, SharePoint, OneDrive, and other standardized integration platforms.

• You will manage the authentication lifecycle, implement retry logic, adhere to rate limits, and navigate the realities of operating within an enterprise network with actual access controls.

• You will create integrations that are both resilient and maintainable, ensuring they are not merely functional in a demonstration environment.

• You will carefully consider how to structure tool availability, manage context across tasks, and develop agent workflows that are reliable and auditable rather than unpredictable.

• You will stay informed about industry advancements and provide well-informed insights on when agentic patterns are appropriate and when they are not.

• As the AI solution portfolio expands, you will establish and uphold the engineering patterns that others will follow, including packaging conventions, versioning, configuration management, logging, and error handling.

• You will develop internal tools that expedite the creation of new solutions while minimizing errors, and you will make architectural decisions that remain valid as the team and codebase grow.

⛳️ Requirements

• Strong proficiency in Python, including data parsing, file I/O, schema validation, subprocess management, packaging, and test creation (pytest or similar).

• Solid understanding of REST API design and usage, including authentication patterns (OAuth, API keys, token refresh), pagination, and error management.

• Familiarity with document parsing libraries such as PyMuPDF, python-docx, openpyxl, pandas, and comparable tools for common enterprise file formats.

• Experience using Git-based development workflows, including branching, versioning, code reviews, and structured release management.

• Knowledge of enterprise integration surfaces, particularly Microsoft 365 (SharePoint, OneDrive, Graph API).

• Practical experience in building the code layer around LLM APIs: structuring prompts programmatically, managing token budgets, parsing and validating model outputs, and gracefully handling failure cases.

• Understanding how structured context, schema-constrained outputs, and validation pipelines enhance AI solution reliability in production.

• Familiarity with document chunking, embedding workflows, and retrieval patterns (RAG), including the tradeoffs associated with retrieval methods for enterprise document types.

• Exposure to agentic patterns, multi-step reasoning pipelines, and tool usage via MCP or similar protocols.

• Experience in developing test infrastructure for systems with probabilistic outputs, including evaluation frameworks, regression suites, and benchmark datasets.

• Comfort in defining "correct" programmatically for outputs that may not have a single correct answer, and building scoring logic that aligns with domain standards.

• An instinct for identifying failure modes: silent errors, schema drift, edge-case documents, and regressions induced by model version changes.

• Experience or significant exposure to construction, commercial real estate, or professional services environments is advantageous.

• Previous experience in a technical role at a professional services firm, PropTech company, or enterprise software organization is relevant.

• You have built something from scratch to gain a deeper understanding of its workings.

• You are comfortable making principled decisions in the absence of established conventions and you document these decisions for the benefit of future team members.

• You hold your technical opinions firmly enough to be constructive, yet flexibly enough to adapt them as needed.

• You are driven by fields where tools are still being developed, and you can influence their evolution.

🏝️ Benefits

• 401(k) plan with company matching contributions.

• Comprehensive medical, dental, and vision care.

• Paid parental leave at 100% of salary.

• Paid time off and company holidays.

• Early access to earned wages through Daily Pay.