Job Description

• Design and implement high-performance RESTful or GraphQL APIs to expose AI model functionality to front-end applications and external services.

• Integrate Large Language Models (LLMs) and custom machine learning models into backend architectures, often using frameworks like LangChain, LangGraph, or FastAPI

• Build and maintain data ingestion pipelines and vector databases (e.g., Pinecone, Weaviate) to support Retrieval-Augmented Generation for AI agents.

• Deploy and orchestrate containerized services using Docker and Kubernetes on cloud platforms like AWS, GCP, or Azure

• Implement real-time monitoring and alerting for AI system health, including tracking model performance, latency, and drift.

• Design testing strategies for non-deterministic AI outputs, including the implementation of guardrails and safety constraints.

• Own the full development lifecycle for services in a production SaaS environment. This includes establishing automated code coverage goals, rigorous code reviews, defining SLOs, and ensuring a fast and effective incident response process

• Works as part of a team.

• Mentor colleagues in AI software engineering best practices
• Performs code reviews of junior software engineers and provides constructive feedback on any findings, both verbal and in writing.

• Read and follow the Underwriters Laboratories Code of Conduct, and follow all physical and digital security practices

• Performs other duties as directed.

• Design and implement high-performance RESTful or GraphQL APIs to expose AI model functionality to front-end applications and external services.

• Integrate Large Language Models (LLMs) and custom machine learning models into backend architectures, often using frameworks like LangChain, LangGraph, or FastAPI

• Build and maintain data ingestion pipelines and vector databases (e.g., Pinecone, Weaviate) to support Retrieval-Augmented Generation for AI agents.

• Deploy and orchestrate containerized services using Docker and Kubernetes on cloud platforms like AWS, GCP, or Azure

• Implement real-time monitoring and alerting for AI system health, including tracking model performance, latency, and drift.

• Design testing strategies for non-deterministic AI outputs, including the implementation of guardrails and safety constraints.

• Own the full development lifecycle for services in a production SaaS environment. This includes establishing automated code coverage goals, rigorous code reviews, defining SLOs, and ensuring a fast and effective incident response process

• Works as part of a team.

• Mentor colleagues in AI software engineering best practices
• Performs code reviews of junior software engineers and provides constructive feedback on any findings, both verbal and in writing.

• Read and follow the Underwriters Laboratories Code of Conduct, and follow all physical and digital security practices

• Performs other duties as directed.

• Advanced technical knowledge and/or software development experience.
• Advanced working knowledge in software application or specific program language requirements of software work.

• University degree in Computer Science or a related discipline.