Senior AI Engineer - LLM & RAG Pipelines

LexisNexis Risk Solutions; Oklahoma City (OK); On-site; USD 95,000 - 159,000; Full time

Benefits offered by this job

Health benefits

401(k) with match

Wellbeing program

Disability insurance

Life insurance

Family benefits

Commuter benefits

Volunteer days

Job summary

LexisNexis Risk Solutions seeks an experienced software engineer to design and deliver production AI systems, including agentic workflows and RAG pipelines, across multiple data sources. You will translate business needs into measurable AI solutions, mentor junior engineers, and collaborate with stakeholders to establish metrics and requirements.

The role emphasizes end-to-end ownership from design to deployment, including evaluation, monitoring, and cost attribution for LLM workloads within AWS

Qualifications

• 5+ years of software engineering experience, including 2+ years building and shipping LLM-based or ML-based systems to production.
• BS in Engineering/Computer Science or equivalent experience required.
• Strong proficiency in TypeScript/Node.js and/or Python; knowledge of C#/.NET is a plus.
• Hands-on experience with LLM platforms and APIs (Azure OpenAI or equivalent).
• Experience building RAG pipelines and structured extraction workflows.

Responsibilities

• Design, build, and deliver production AI systems including agentic workflows and RAG services across multiple business units.
• Interface with stakeholders to scope AI use cases, define success metrics, and finalize requirements.
• Write and review detailed technical specifications and architecture documents.
• Build and maintain evaluation harnesses; monitor accuracy, latency, and cost in production.
• Own cost and performance of LLM workloads including model selection and caching.
• Build CI/CD pipelines and deployment automation for AI services in AWS and Azure.
• Mentor less‑senior engineers on AI system design and production readiness.

Job description

LexisNexis Risk Solutions seeks an experienced software engineer to design and deliver production AI systems, including agentic workflows and RAG pipelines, across multiple data sources. You will translate business needs into measurable AI solutions, mentor junior engineers, and collaborate with stakeholders to establish metrics and requirements.

The role emphasizes end-to-end ownership from design to deployment, including evaluation, monitoring, and cost attribution for LLM workloads within AWS