Technical AI Lead, Leasing, Legal, Document Generation/Administration

Job Title

Technical AI Lead, Leasing, Legal, Document Generation/Administration

Company

Prologis

Title

Technical AI Lead, Leasing, Legal, Document Generation/Administration

Location

Denver or Remote

A day in the life

Prologis is scaling responsible AI across its business, and the Technical AI Lead for Leasing, Legal, and Document Generation/Administration will lead the technical strategy and delivery of AI‑enabled capabilities that improve critical business workflows. This hands‑on technical leadership role will partner with business leaders, product teams, engineers, architects, data teams, and technology partners to move high‑value opportunities from discovery and rapid prototyping through production deployment and adoption. The role will define how generative AI, agentic workflows, enterprise data, CRM, document intelligence, and integrations come together to solve business problems. Success requires combining deep technical expertise with business process understanding to deliver solutions that are accurate, secure, scalable, trusted by users, and capable of producing measurable business outcomes.

Key Responsibilities Include

• Lead AI initiatives end to end, from business discovery and solution design through prototyping, architecture, integration, evaluation, implementation, rollout, adoption, and continuous improvement.
• Identify and frame high‑value AI opportunities with business stakeholders, determining when generative AI, agentic automation, document intelligence, traditional automation, or process redesign is the appropriate solution.
• Define scalable AI solution architectures connecting LLMs, AI agents, retrieval‑augmented generation (RAG), enterprise data, Salesforce, APIs, document platforms, workflow orchestration, and other enterprise systems.
• Design agentic AI workflows that securely retrieve enterprise information, reason over business context, invoke tools and APIs, and execute multi‑step processes with appropriate human oversight, controls, and guardrails.
• Establish enterprise RAG and knowledge architecture patterns for document ingestion, metadata, embeddings, vector and hybrid search, grounding, citations, access‑controlled retrieval, and retrieval evaluation.
• Lead document intelligence solutions supporting lease abstraction, contract analysis, clause identification, obligation extraction, risk analysis, document classification, document generation, and related Leasing and Legal workflows.
• Provide technical direction and coordinate delivery across engineers, architects, vendors, and onshore/offshore teams, establishing engineering standards, conducting architecture and design reviews, resolving complex technical challenges, and aligning stakeholders on dependencies, risks, data readiness, timelines, and adoption.

Required

• 7+ years of experience in technical leadership, software engineering, enterprise architecture, AI delivery, CRM, data platforms, digital transformation, or closely related enterprise technology disciplines.
• Demonstrated experience designing and delivering generative AI solutions incorporating LLMs, agentic workflows, RAG, enterprise search, document intelligence, prompt engineering, or workflow orchestration.
• Ability to prototype, evaluate, and troubleshoot AI solutions using technologies such as Node, Python, SQL, APIs, JSON, enterprise AI platforms, and modern AI development tools.
• Experience designing agentic workflows incorporating enterprise retrieval, reasoning, tool or API execution, orchestration, state management, human oversight, guardrails, and evaluation.
• Experience establishing systematic methods to evaluate AI accuracy, groundedness, hallucination risk, retrieval quality, extraction performance, agent or tool execution, and end‑to‑end workflow success.
• Experience applying AI to complex unstructured content, including leases, contracts, legal documents, or other enterprise documents.
• Experience with complex Salesforce or CRM environments and related data, integrations, workflows, automation, reporting, or AI capabilities.
• Experience integrating structured and unstructured enterprise data through APIs, middleware, ETL/ELT, data pipelines, and data platforms such as Snowflake or comparable technologies.
• Demonstrated experience moving AI solutions from prototypes into production, including testing, deployment, monitoring, governance, support, and continuous improvement.