Senior GenAI Engineer – Private LLM & AI Platform Lead

K57 Skechers USA, Inc.; Rancho Santa Margarita (CA); On-site; USD 108,000 - 180,000; Full time

Job summary

Skechers USA, Inc. in California seeks a Senior GenAI Engineer to lead the design, development and deployment of private GenAI tools and LLM platforms, collaborating across security, data, architecture and business units.

You’ll build scalable AI pipelines on AWS (Bedrock, Lambda, EC2, S3), apply RAG and GDPR-compliant practices, and implement GenAI workflows with Python, FastAPI and React-based frontends. Strong collaboration and problem-solving required.

Qualifications

• BS/MS in Computer Science, Machine Learning, AI, or equivalent.
• 8+ years of software development experience, including some with generative models.
• 3+ years of backend Python development.
• 5+ years building REST APIs with JSON/HTTP.
• 5+ years frontend JS/HTML/CSS with a framework (React or Vue).
• Familiarity with websockets.

Responsibilities

• Lead design, development, and deployment of Skechers GenAI tools and private LLMs.
• Coordinate with security, UI/UX, data integration, architecture, and business units.
• Develop and maintain scalable AI pipelines using AWS (Bedrock, Lambda, EC2, S3).
• Apply RAG, keyword vector search, and GDPR-compliant data handling in daily tasks.
• Build and deploy frontend/back-end services using Python (FastAPI) and React/React Native.
• Stay current with GenAI advancements and apply them to business problems.

Skills

AWS Bedrock

LLMs

Stable Diffusion

Python

FastAPI

Node.js

React

React Native

JIRA/Confluence

SQL/NoSQL

Git/Jenkins

Education

CS/ML/MS degree

Tools

Docker

Kubernetes

Terraform

Job description

Skechers USA, Inc. in California seeks a Senior GenAI Engineer to lead the design, development and deployment of private GenAI tools and LLM platforms, collaborating across security, data, architecture and business units.

You’ll build scalable AI pipelines on AWS (Bedrock, Lambda, EC2, S3), apply RAG and GDPR-compliant practices, and implement GenAI workflows with Python, FastAPI and React-based frontends. Strong collaboration and problem-solving required.