AI Engineer

Job Description

Job Description

MONTICELLOAM, LLC and its affiliates ("Monticello") is a real estate and asset-based lender providing asset management and comprehensive capital solutions for healthcare, multifamily, and commercial real estate assets throughout the US. Monticello is seeking team players who can work in a collaborative environment and possess drive, integrity, creativity, compassion, and a strong work ethic.

We're looking for an AI Engineer to build and deploy intelligent systems across asset management, underwriting, and business intelligence: from document extraction pipelines to AI-powered internal tools to autonomous workflows that reduce manual work across the business.

This role is fully in person. Candidates must be able to work onsite at our New York City office.

Our Tech Stack

At Monticello, we build and operate our data platform using modern, scalable technologies. As an AI Engineer, you'll work hands-on with:

AI & Machine Learning

• Large Language Models (via AWS Bedrock) for document extraction, analysis, and autonomous decision support
• RAG architecture, prompt engineering, and agent-based workflows
• Python as the backbone for everything: AI pipelines, automation, backend services, and data engineering

Data Platform

• Snowflake as our cloud data warehouse
• DBT for modular, version-controlled data transformations
• Airflow for orchestration and workflow automation
• SQL for data modeling and analysis'

Infrastructure & Delivery

• AWS for cloud infrastructure and services
• React for front-end interfaces and internal tools
• Git for version control and CI/CD

About Your Team

You'll join a small, collaborative team that builds the AI and data systems powering Monticello's real estate finance operations.

What you'll own:

• Designing and building AI pipelines that extract structured data from unstructured documents — loan files, appraisals, financial statements, invoices
• Building full-stack internal tools (React + Python APIs) that put AI capabilities directly in front of business users
• Developing RAG systems, AI agents, and automated workflows that reduce manual work across asset management

You'll also contribute to:

• Data pipelines and warehouse architecture (Snowflake, Airflow, dbt)
• Data quality frameworks and automated QC processes
• Dashboards and reporting that drive portfolio decisions

Qualifications

Core:

• Experience building and deploying AI/ML solutions end to end
• Strong Python skills across AI/ML, data engineering, and backend services
• Hands-on experience with LLMs — agent orchestration, prompt engineering, RAG architecture, document extraction pipelines
• Familiarity with cloud AI services (AWS Bedrock preferred)

Also valuable:

• SQL and data modeling experience (Snowflake, dbt)
• Front-end development with React
• Experience working directly with non-technical stakeholders

We welcome applicants from a variety of educational and professional backgrounds. If you've built and shipped AI solutions and are looking to own projects end to end, we'd like to hear from you — even if your experience doesn't match every bullet.

What We Value

• Intellectual curiosity and a passion for solving complex problems.
• Balancing technical quality with practical business impact.
• An entrepreneurial mindset and creative approach to innovation and continuous improvement.

At Monticello, we believe that finding creative solutions for our clients comes from the collaboration of people with diverse backgrounds and perspectives. We strive to build an inclusive work environment that celebrates differences and empowers all individuals with opportunities to channel their entrepreneurial spirit. Monticello is an EEO employer.

Monticello offers a competitive base salary and bonus. The base salary range for this position is $110,000-$140,000/year. The specific compensation that will be offered is based on an understanding of the hired candidates' qualifications at the time of hire. Employees are eligible for a discretionary bonus based on employee work performance reviewed during the annual review process.