GenAI Production Systems Engineer II

Niagara Bottling; Diamond Bar (CA); On-site; USD 100,000 - 146,000; Full time

Benefits offered by this job

Medical and dental

401(k) with match

Paid time off

Tuition assistance

Family building benefits

Job summary

Niagara Bottling is seeking a GenAI Software Engineer II (Production Systems) to advance AI/ML driven enterprise solutions. You will design and deploy scalable APIs, microservices, and middleware with embedded AI capabilities, collaborating across IT, data science, and ERP teams.

You will apply ML models, LLMs, and intelligent automation to real business problems, emphasizing reliable deployment, monitoring, and continuous improvement in a production environment.

Qualifications

• 4–5 years hands-on AI/ML/GenAI in production.
• Experience with ML models, LLMs, prompt engineering.
• Experience with intelligent monitoring, anomaly detection, predictive workflows.
• Experience with Node.js and Python in production.
• Cloud deployment of AI/ML services (Azure preferred, AWS acceptable).
• Experience with enterprise integrations and middleware (MuleSoft).
• Experience building scalable APIs/microservices.

Responsibilities

• Develop AI/ML powered enterprise solutions and middleware.
• Design, develop and maintain scalable APIs and microservices.
• Embed AI/ML capabilities into integration workflows.
• Collaborate with IT, data science, ERP teams and partners.
• Deploy and monitor AI-enabled systems; troubleshoot issues.
• Follow SDLC processes and document designs.

Skills

AI/ML engineering

GenAI

LLMs

Node.js

Python

Microservices

APIs

DevOps practices

CI/CD

Data pipelines

Education

Bachelor's Degree in CS/Engineering

Master's Degree (Preferred)

Tools

MuleSoft

Azure

AWS

OpenAI

Hugging Face

Job description

Niagara Bottling is seeking a GenAI Software Engineer II (Production Systems) to advance AI/ML driven enterprise solutions. You will design and deploy scalable APIs, microservices, and middleware with embedded AI capabilities, collaborating across IT, data science, and ERP teams.

You will apply ML models, LLMs, and intelligent automation to real business problems, emphasizing reliable deployment, monitoring, and continuous improvement in a production environment.