[Remote] AI Product Engineer

Note: The job is a remote job and is open to candidates in USA. JLL is a leading global provider of real estate and investment management services, committed to shaping the future of real estate through technology and innovation. They are seeking an AI Product Engineer to develop and maintain the engineering infrastructure behind AI solutions for construction and real estate project delivery, ensuring reliability and scalability of AI-powered tools.ResponsibilitiesYou're a solution developer, but you’re also an engineer who builds the infrastructure that makes AI solutions reliable, scalable, and maintainableYou'll develop deep familiarity with the information landscape of construction and real estate project delivery, understanding what data exists, where it lives, what form it takes, and what has to happen before an AI model can do something useful with itYou'll design the structured output contracts that govern what AI solutions produce and build the validation logic that enforces themWhen a solution produces unexpected output or degrades silently on an unusual document, you'll own the detection and recovery logicYou'll define what production-ready looks like before building begins, run solutions against diverse real-world document sets, and maintain quality as the underlying models and input corpus evolve over timeYou'll connect AI solutions to JLL's enterprise environment using REST APIs, Microsoft Graph, SharePoint, OneDrive, and other standard integration surfacesYou'll handle authentication lifecycle, retry logic, rate limits, and the realities of operating inside an enterprise network with real access controlsYou'll design integrations that are resilient and maintainable, not just functional in a demo environmentYou'll design and build multi-step reasoning pipelines that connect models to enterprise tools and data through the Model Context Protocol and similar agentic infrastructureYou'll think carefully about how to structure tool availability, manage context across steps, and build agent workflows that are reliable and auditable rather than unpredictableAs the AI solution portfolio grows, you'll establish and maintain the engineering patterns others follow: packaging conventions, versioning, configuration management, logging, and error handlingYou'll write internal tooling that makes building new solutions faster and less error-prone, and you'll make architectural decisions that hold up as the team and codebase scaleSkillsStrong Python proficiency: data parsing, file I/O, schema validation, subprocess management, packaging, and test authoring (pytest or similar)Solid understanding of REST API design and consumption, including auth patterns (OAuth, API keys, token refresh), pagination, and error handlingComfort with document parsing libraries: PyMuPDF, python-docx, openpyxl, pandas, and equivalent tools for common enterprise file formatsExperience with Git-based development workflows: branching, versioning, code review, and structured release managementFamiliarity with enterprise integration surfaces, particularly Microsoft 365 (SharePoint, OneDrive, Graph API)Hands-on experience building the code layer around LLM APIs: structuring prompts programmatically, managing token budgets, parsing and validating model outputs, and handling failure cases gracefullyUnderstanding of how structured context, schema-constrained outputs, and validation pipelines improve AI solution reliability in productionFamiliarity with document chunking, embedding workflows, and retrieval patterns (RAG), including the tradeoffs between retrieval approaches for enterprise document typesExposure to agentic patterns, multi-step reasoning pipelines, and tool use via MCP or similar protocolsExperience building test infrastructure for systems with probabilistic outputs: evaluation frameworks, regression suites, benchmark datasetsComfort defining 'correct' programmatically for outputs that don't have a single right answer, and building scoring logic that reflects domain standardsInstinct for failure modes: silent errors, schema drift, edge-case documents, and model-version-induced regressionsExperience in or meaningful exposure to construction, commercial real estate, or professional services environments is a plusPrior work in a technical role at a professional services firm, PropTech company, or enterprise software organization is relevant backgroundYou've built something from scratch specifically to understand how it workedYou're comfortable making principled decisions in the absence of established conventions, and you document those decisions so the next person understands the reasoningYou hold your technical opinions firmly enough to be useful and loosely enough to update themYou're energized by fields where the tooling is still being invented and you can influence how it developsBenefits401(k) plan with matching company contributionsComprehensive Medical, Dental & Vision CarePaid parental leave at 100% of salaryPaid Time Off and Company HolidaysEarly access to earned wages through Daily PayCompany OverviewWe’re a leading professional services firm that specializes in real estate and investment management. It was founded in 1783, and is headquartered in Chicago, Illinois, USA, with a workforce of 10001+ employees. Its website is http://www.jll.com/.