AI and LLM Lead

Description

• Technical Guidance: Provide peer mentorship, code and architecture reviews, and technical best practices to elevate the broader team’s capabilities
• 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.
• Deployment & Monitoring: Work with MLOps and engineering teams to deploy models into production environments, establish monitoring frameworks, and continuously improve solution performance.
• 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.
• 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.

Requirements

• 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 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.
• Solid understanding of cloud platforms (AWS, Azure, GCP) and MLOps tools.
• Demonstrated ability to convert business questions into deployable ML solutions and explain results to non-experts.
• Proficient in Python (pandas, scikit-learn, PyTorch/TensorFlow, Langchain etc), SQL, and version control.
• Proven success owning and delivering cross-functional AI/LLM projects from start to finish.
• 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.

Benefits

Training + Development

Interview process

Visa Sponsorship

Security clearance