With our expertise, we are producing the best available data, workflow tools, technology, and analytics to support everyone making a real estate decision. There has never been a better time to make an impact and we invite you to join us on this journey.
LightBox is a leading provider of data and workflow solutions across commercial real estate and location intelligence. Our solutions deliver the depth, speed and accuracy that enable insights to over 50,000 brokers, 1,000 banks and lenders, 1,000 law firms and 5,000 environmental consulting and engineering firms.
As Senior Engineer - AI Platform, you will build the self-service infrastructure and developer-facing tooling that makes LightBox's AI capabilities accessible and reliable. You'll work across the stack - from MCP server implementations and API gateways to internal developer tools and integration surfaces - ensuring that AI models and agents can be composed, deployed, and consumed by both internal teams and external partners.
This role sits at the intersection of platform engineering and applied AI. You won't just be writing CRUD endpoints; you'll be building the connective tissue between proprietary CRE data, specialized AI models, and the production applications that hundreds of thousands of industry professionals use daily.
What You'll Do- Design and implement API gateways, model routing layers, and service infrastructure that connect fine-tuned CRE models with production applications.
- Develop internal developer tooling - prompt management interfaces, evaluation dashboards, trace viewers - that accelerate the team's ability to ship and iterate on AI features.
- Implement integration patterns for embedding AI capabilities into LightBox's existing workflow applications, ensuring seamless user experiences.
- Build observability and monitoring infrastructure for AI workloads: latency tracking, quality metrics, cost attribution, and usage analytics.
- Contribute to the platform's knowledge retrieval and caching layers, optimizing for both accuracy and performance at scale.
- Collaborate with ML engineers on model serving infrastructure, helping bridge the gap between training outputs and production deployment.
- Participate in architecture decisions and technical design reviews, bringing a pragmatic full-stack perspective to the team's work.
- 6+ years of software engineering experience with a strong full-stack foundation (backend-leaning preferred).
- Production experience building APIs, microservices, and platform infrastructure in Python and/or TypeScript/Node.js.
- Solid understanding of cloud infrastructure (AWS or GCP), containerization (Docker, Kubernetes), and CI/CD pipelines.
- Experience building developer-facing tools, SDKs, or internal platforms that other engineers consume.
- Familiarity with LLM integration patterns: API-based model consumption, prompt engineering, retrieval-augmented generation, and tool/function calling.
- Strong fundamentals in distributed systems, data modeling, and API design.
- Comfort working in a small team with high autonomy - you can own a feature end-to-end from design through deployment and monitoring.
- Experience with Model Context Protocol (MCP) or similar structured AI integration standards.
- Background in data-intensive platforms, particularly property data, geospatial systems, or document processing pipelines.
- Familiarity with model serving frameworks (LiteLLM, OpenRoute, AWS BedRock) and inference optimization.
- Experience in real estate technology, financial services, or other regulated/document-heavy industries.
- Prior work building observability or evaluation tooling for ML/AI systems.
Skills Required
- 6+ years of software engineering experience
- Strong full-stack foundation, preferably backend-leaning
- Production experience building APIs, microservices, and platform infrastructure
- Proficiency in Python and/or TypeScript/Node.js
- Understanding of AWS or GCP cloud infrastructure
- Experience with Docker and Kubernetes
- Experience with CI/CD pipelines
- Experience building developer-facing tools, SDKs, or internal platforms
- Familiarity with LLM integration, prompt engineering, retrieval-augmented generation, and tool/function calling
- Strong fundamentals in distributed systems, data modeling, and API design
- Ability to own features end-to-end from design through deployment and monitoring
- Experience with Model Context Protocol or similar AI integration standards
- Background in data-intensive platforms, property data, geospatial systems, or document processing pipelines
- Familiarity with LiteLLM, OpenRouter, AWS Bedrock, or inference optimization
- Experience in real estate technology, financial services, or regulated/document-heavy industries
- Experience building observability or evaluation tooling for ML/AI systems
What We Do
LightBox is a commercial real estate data and technology platform that connects property information, professionals, and workflows. It combines property, parcel, location, ownership, transaction, and environmental data with tools for researching, valuing, financing, assessing, marketing, and transacting real estate. Its products support brokers, developers, investors, lenders, insurers, government agencies, and environmental and valuation professionals, helping them make informed decisions with authoritative data, integrated workflows, and location intelligence.
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