Principal AI Engineer - Enterprise Generative AI & LLMs

MACHINE LEARNING TECHNOLOGIES LLC; Waukesha (WI); On-site; USD 150,000 - 230,000; Full time

Job summary

MACHINE LEARNING TECHNOLOGIES LLC is seeking a seasoned Principal AI Engineer to lead architecture and deployment of Generative AI solutions in a Waukesha, WI setting. You will build sophisticated AI agents and integrate high-scale LLMs into enterprise ecosystems to solve complex business challenges.

Responsibilities include designing scalable AI solutions, developing multi-agent systems, and optimizing LLM performance, with cloud pipelines across AWS, Azure, or GCP, while collaborating with

Qualifications

• 12+ years of software engineering and data science experience.
• Expert proficiency in Python and backend integration.
• Deep expertise in LLMs such as GPT and Claude.
• Proven experience with orchestration tools and agentic frameworks.
• Strong foundation in AI/ML life cycles.

Responsibilities

• Design and implement scalable AI solutions using LangChain and AutoGen.
• Develop multi-agent orchestration systems with memory and reasoning.
• Optimize LLM performance via prompt engineering and hyperparameter tuning.
• Architect robust cloud pipelines (AWS/Azure/GCP) for deployment.
• Collaborate with cross-functional teams to integrate AI into products.

Skills

Python programming

LLMs expertise

Prompt engineering

Agent orchestration

Cloud deployment

Backend integration

LangChain

AutoGen

LlamaIndex

Education

Advanced degree in CS or related field

Tools

LangChain

AutoGen

LlamaIndex

Job description

MACHINE LEARNING TECHNOLOGIES LLC is seeking a seasoned Principal AI Engineer to lead architecture and deployment of Generative AI solutions in a Waukesha, WI setting. You will build sophisticated AI agents and integrate high-scale LLMs into enterprise ecosystems to solve complex business challenges.

Responsibilities include designing scalable AI solutions, developing multi-agent systems, and optimizing LLM performance, with cloud pipelines across AWS, Azure, or GCP, while collaborating with