AI and LLM Lead

Overview

Lead AI/LLM initiatives, designing and deploying generative AI solutions for logistics and real‑estate challenges.

Key Responsibilities

• ai lifecycle
• llm development
• model development
• model monitoring
• cross functional
• insight communication

Tasks

-Lead AI Project Lifecycle: Own the end-to-end process from ideation and stakeholder engagement to deployment and post-launch evaluation of AI and LLM initiatives. -LLM Application Development: Design and implement LLM powered applications, tailored to solve real estate-specific challenges (e.g., lease abstraction, tenant communication, investment memo summarization). Craft prompts, fine-tune domain-specific models, or create retrieval-augmented generation pipelines that boost productivity or customer experience. -Business Use Case Translation: Collaborate with business leaders to translate strategic goals into AI opportunities. Engage with functional leads to clarify requirements, convert them into analytical hypotheses, rapidly prototype and iterate solutions. -Cross-Functional Collaboration: Act as a bridge between product, engineering, data, and business teams to align AI solutions with user needs and technical feasibility. -Continuous Improvement: Stay current on emerging algorithms and tooling; propose POCs that can raise model accuracy, reduce latency, or lower cost. -Insight Communication: Distill findings into clear narratives and visualizations; present recommendations to technical and non-technical stakeholders. -Model Development & Deployment: Execute the full data-science workflow—including EDA, feature engineering, model selection, validation, and monitoring—adhering to team coding and documentation standards. -Deployment & Monitoring: Work with MLOps and engineering teams to deploy models into production environments, establish monitoring frameworks, and continuously improve solution performance. -Technical Guidance: Provide peer mentorship, code and architecture reviews, and technical best practices to elevate the broader team’s capabilities

Requirements

• python
• llm fine-tuning
• vector db
• mlops
• deep learning
• cloud platforms

What You Bring

-Proven success owning and delivering cross-functional AI/LLM projects from start to finish. -Solid grounding in supervised, unsupervised, and deep learning techniques plus model-evaluation best practices. -Hands-on experience fine-tuning or integrating LLMs such as GPT, LLaMA, Claude, Gemini, etc. -Experience working in or supporting real estate, logistics, finance, or other asset-heavy industries is a plus. -Experience with vector databases (e.g., FAISS, Pinecone), embeddings, and RAG architectures. -3+ years of hands-on experience in applied AI/ML development (1-2 years with advanced STEM degree), with at least 2 years focusing on LLMs or NLP-related technologies. -Solid understanding of cloud platforms (AWS, Azure, GCP) and MLOps tools. -Proficient in Python (pandas, scikit-learn, PyTorch/TensorFlow, Langchain etc), SQL, and version control. -Demonstrated ability to convert business questions into deployable ML solutions and explain results to non-experts.

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About Prologis

-Provides prime distribution centers and warehouses for industries like retail, e-commerce, automotive, and technology. -Focuses on designing and managing large-scale logistics facilities that support global supply chains. -Leverages innovative technologies to optimize warehouse operations and drive efficiency for tenants. -Expands its footprint through strategic acquisitions in key logistics hubs worldwide. -Develops sustainable and adaptable logistics environments for future growth. -Known for quickly adapting to changing market demands, ensuring resilience in operations.

Sector Specialisms

Security Clearance

-all job offers are contingent upon successful completion of background verification.