Senior AI Engineer – Enterprise AI & Cloud-Native Solutions

rlicorp; Peoria (AZ); On-site; USD 150,000 - 210,000; Full time

Job summary

RLI Corp seeks an accomplishedSenior AI Software Engineer to design, develop, and deploy AI-enabled enterprise applications. You will build scalable backend services, REST APIs, and cloud-native solutions that integrate AI capabilities with enterprise systems.

Responsibilities include implementing LLM-based workflows, vector search, and knowledge ingestion, while collaborating with product owners and engineers to deliver robust, secure, and maintainable AI solutions.

Qualifications

• 7+ years of professional software engineering experience
• Experience designing cloud-native distributed systems
• Experience with AI-enabled applications using LLMs and RAG
• Experience with vector databases, embeddings, and semantic retrieval
• Experience with API design, microservices, and event-driven architectures

Responsibilities

• Design, develop, test, and deploy enterprise AI applications
• Build AI-enabled solutions using LLMs, RAG, and AI agents
• Develop scalable backend services and REST APIs
• Integrate enterprise systems via APIs, messaging, and event-driven patterns
• Create vector search, semantic retrieval, and knowledge ingestion pipelines
• Deploy and manage applications in Kubernetes environments
• Collaborate with stakeholders to translate business needs into AI-enabled solutions
• Participate in architecture reviews, code reviews, Agile ceremonies, and CI/CD pipelines
• Mentor team members and promote code quality, security, and observability

Skills

Python

Cloud-native

LLMs

Vector search

REST APIs

Kubernetes

CI/CD

Agile

Education

Bachelor's degree in computer science or related field

Tools

Git

GitLab

Docker

Kubernetes

Job description

RLI Corp seeks an accomplishedSenior AI Software Engineer to design, develop, and deploy AI-enabled enterprise applications. You will build scalable backend services, REST APIs, and cloud-native solutions that integrate AI capabilities with enterprise systems.

Responsibilities include implementing LLM-based workflows, vector search, and knowledge ingestion, while collaborating with product owners and engineers to deliver robust, secure, and maintainable AI solutions.