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Generative Ai Developer Jobs in Minnesota (NOW HIRING)

Pwc

AI Engineer

Minneapolis, MN · On-site

$50K - $112K/yr

... generative AI techniques, including prompt engineering, LLM evaluation, and fine-tuning, to develop production-ready applications powered by foundation models - Developing automated evaluation ...

ESP IT

Data Arch/AI Engineer

Eden Prairie, MN · On-site

Data Architect & AI Engineer - Microsoft Fabric We are seeking a Data Architect / AI Engineer to ... Experience working with LLM APIs, generative AI platforms, or AI tool deployment in a business ...

Worky

Cloud/AI Engineer

Virginia, MN · On-site

$150 - $230/hr

... Generative AI capabilities including Large Language Models (LLMs), Retrieval-Augmented Generation ... Expertise implementing CI/CD pipelines, DevOps practices, infrastructure automation (e.g ...

Target

Principal Engineer - AI /ML Platform

Minneapolis, MN · On-site

$168K - $356K/yr

Experience building developer platforms or internal platform products * Experience with distributed ... Familiarity with Generative AI platforms and infrastructure supporting foundation model workloads

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How much do generative ai developer jobs pay per hour?

As of Aug 22, 2026, the average hourly pay for generative ai developer in Minnesota is $44.36, according to ZipRecruiter salary data. Most workers in this role earn between $23.08 and $53.70 per hour, depending on experience, location, and employer.

What is a generative AI developer?

A Generative AI Developer is a technology professional who specializes in designing, building, and deploying artificial intelligence systems that can create new content, such as text, images, audio, or code. They work with advanced machine learning models, like generative adversarial networks (GANs) or large language models, to enable computers to produce original outputs. These developers often collaborate with data scientists, researchers, and product teams to integrate AI-generated content into software applications and business solutions.

What are the key skills and qualifications needed to thrive as a generative AI developer?

To thrive as a Generative AI Developer, you need strong programming skills (especially in Python), a deep understanding of machine learning concepts, and an advanced degree in computer science or a related field. Familiarity with frameworks like TensorFlow, PyTorch, and experience with cloud platforms or model deployment tools are typically required. Creative problem-solving, adaptability, and effective collaboration are standout soft skills in this evolving field. These abilities are crucial to design, implement, and refine generative models that solve real-world problems and drive innovation.

What are some common challenges faced by generative AI developers when deploying models in production environments?

Generative AI Developers often encounter challenges such as ensuring model reliability, managing computational resource requirements, and addressing ethical considerations like data bias or content safety. Deploying generative models at scale requires robust monitoring to detect unexpected outputs or model drift, and collaboration with data engineers and product teams to optimize performance. Staying up-to-date with evolving frameworks and best practices is essential, as production environments demand both technical rigor and adaptability to new AI advancements.

What is the difference between Generative Ai Developer vs Machine Learning Engineer?

AspectGenerative Ai DeveloperMachine Learning Engineer
CredentialsBachelor's or higher in CS, AI, or related fields; experience with deep learning frameworksBachelor's or higher in CS, Data Science, or related fields; strong programming skills
Work EnvironmentDevelops AI models for content creation, chatbots, and creative applicationsBuilds and deploys ML models for various data-driven solutions across industries
Industry UsageTech, entertainment, marketing, and creative sectorsFinance, healthcare, tech, and e-commerce sectors

While both roles involve AI and machine learning, Generative Ai Developers focus on creating models that generate content, such as images or text, whereas Machine Learning Engineers develop broader ML solutions for diverse applications. The roles often overlap but differ mainly in their specific focus areas and use cases.

How to become a generative AI developer?

To become a generative AI developer, you should have a strong foundation in programming languages like Python, experience with machine learning frameworks such as TensorFlow or PyTorch, and knowledge of neural network architectures like transformers. Gaining expertise in natural language processing and deep learning, along with practical experience through projects or internships, is essential. Certifications in AI or data science can also enhance your qualifications.

What are popular job titles related to Generative Ai Developer jobs in Minnesota?

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What job categories do people searching Generative Ai Developer jobs in Minnesota look for?

The top searched job categories for Generative Ai Developer jobs in Minnesota are:

What cities in Minnesota are hiring for Generative Ai Developer jobs?

Cities in Minnesota with the most Generative Ai Developer job openings:

Generative Ai Developer jobs near you
Infographic showing various Generative Ai Developer job openings in Minnesota as of August 2026, with employment types broken down into 76% Full Time, 20% Part Time, 3% Contract, and 1% Nights. Highlights an 63% Physical, 4% Hybrid, and 33% Remote job distribution, with an average salary of $92,260 per year, or $44.4 per hour.

Senior GenAI Architect & Consultant (Virginia)

Lovelytics, LLC

Virginia, MN • On-site

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Lovelytics, LLC is seeking a Generative AI Engineer (Senior Consultant) to design and deploy GenAI solutions in production for client engagements. You will lead projects involving RAG pipelines, vector databases, LLM orchestration, and AI agents on the Databricks platform.

Join a growing AI practice to build production-grade GenAI systems, optimize end-to-end workflows, and collaborate with cross-functional teams while shaping architecture, governance, and security across client implementations.

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