GenAI Engineer: Enterprise AI & RAG on AWS (Hybrid)

System One; Lafayette (LA); Hybrid; USD 120,000 - 180,000; Full time

Job summary

System One in Lafayette, LA seeks a GenAI Engineer to design and deliver production-ready AI applications and agent-based solutions across a regulated enterprise environment. You will work with LLMs, semantic search, RAG, and tool integrations to translate business needs into measurable AI capabilities.

This hybrid role combines onsite collaboration with remote work and requires strong Python and cloud skills.

Qualifications

• 5+ years of experience with context engineering and enterprise data management.
• Strong Python development with APIs, Git, testing, and CI/CD.
• Experience building production AI apps in AWS/cloud-native environments.
• Hands-on experience with Generative AI, LLMs, prompt engineering, and RAG.
• Experience designing AI agents and orchestration across multi-agent workflows.
• Knowledge of AI governance, guardrails, security, and compliance.
• Understanding of integration with enterprise systems via REST APIs.

Responsibilities

• Build and support production-ready AI applications and agent-based solutions.
• Translate business needs into measurable AI capabilities.
• Ensure security, governance, and compliance in AI solutions.
• Work in AWS cloud environments on enterprise-grade AI applications.
• Collaborate with teams to integrate with enterprise systems and tools.

Skills

Context engineering

Semantic search

Metadata handling

Knowledge retrieval

Data lineage

Spec driven development

Python development

APIs

CI/CD

AWS cloud

LLM applications

Prompt engineering

AI agents

Orchestration

Guardrails

Responsible AI

Security controls

Education

Bachelor's degree in CS/Engineering/IT/Data Science

Tools

Git

Automated testing

CI/CD

REST APIs

Workflow platforms

AWS

Job description

System One in Lafayette, LA seeks a GenAI Engineer to design and deliver production-ready AI applications and agent-based solutions across a regulated enterprise environment. You will work with LLMs, semantic search, RAG, and tool integrations to translate business needs into measurable AI capabilities.

This hybrid role combines onsite collaboration with remote work and requires strong Python and cloud skills.