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.

Requirements

Must-haves

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.
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.

Nice-to-haves

6+ years of software engineering experience with a strong full-stack foundation (backend-leaning 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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiapimlpythontypescriptnode.jsawsgcpdockerkubernetesci/cdllm

Platform: dayforce
Effort: medium