Engineering - Data Platform - Senior Software Engineer

LightBox Employer; Irvine (CA); On-site; USD 140,000 - 200,000; Full time

Job summary

LightBox Employer is seeking a Senior Engineer - AI Platform to build self-service infrastructure and developer-facing tooling for AI capabilities. You will work across MCP servers, API gateways, and internal tools to enable reliable model composition, deployment, and consumption by internal teams and partners.

You'll collaborate with ML engineers on model serving, design scalable platform components, and contribute to architecture decisions, all while owning features end-to-end from design to

Qualifications

• 6+ years of software engineering experience with a strong full-stack foundation.
• Production experience building APIs, microservices, and platform infrastructure.
• Familiarity with LLM integration patterns and AI workflows.

Responsibilities

• Design and implement API gateways and service infrastructure for CRE models.
• Develop internal tooling and dashboards to accelerate AI feature delivery.
• Implement integration patterns for embedding AI into existing workflows.
• Build observability and monitoring for AI workloads, including latency and cost analytics.
• Improve knowledge retrieval and caching for accuracy and performance at scale.
• Collaborate with ML engineers on model serving and production deployment.
• Participate in architecture decisions and technical design reviews.

Skills

Full-stack engineering

APIs & microservices

Python

TypeScript/Node.js

Cloud infrastructure

Docker

Kubernetes

CI/CD

LLM integration

Distributed systems

API design

Tools

LiteLLM

OpenRoute

AWS BedRock

Job description

The Role

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.

What We're Looking For

Required

• 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.

Preferred

• 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.