ZipRecruiter

Log In

1

Generative Ai Developer Jobs in Michigan (NOW HIRING)

Judge Group, Inc.

Midlevel AI Developer

Ann Arbor, MI · On-site

$50 - $55/hr

As an AI Engineer, you will design, develop, and deploy AI-powered applications and services that ... Build and integrate Generative AI solutions using Large Language Models (LLMs). Develop AI agents ...

next page

Showing results 1-20

All JobsGenerative Ai Developer JobsGenerative Ai Developer Jobs in Michigan

Generative Ai Developer information

See Michigan salary details

$16

$39

$87

How much do generative ai developer jobs pay per hour?

As of Sep 10, 2026, the average hourly pay for generative ai developer in Michigan is $39.47, according to ZipRecruiter salary data. Most workers in this role earn between $20.53 and $47.79 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 Michigan?

For Generative Ai Developer jobs in Michigan, the most frequently searched job titles are:

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

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

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

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

Generative Ai Developer jobs near you
Infographic showing various Generative Ai Developer job openings in Michigan as of August 2026, with employment types broken down into 70% Full Time, 28% Part Time, and 2% Contract. Highlights an 63% Physical, 4% Hybrid, and 33% Remote job distribution, with an average salary of $82,104 per year, or $39.5 per hour.

Generative AI Researcher

Warren, MI • On-site

Tata Consultancy Services
See more jobs at Tata Consultancy Servicesexternal

Full-time

Re-posted 21 days ago

Tata Consultancy Services rating

6.5

Company rating: 6.5 out of 10

Based on 21 frontline employees who took The Breakroom Quiz

176th of 226 rated it services

Job description

Job Summary:

We are seeking a highly skilled and creative Entry Level - Generative AI Engineer to apply state-of-the-art generative models to solve complex challenges in automotive engineering. This role focuses on creating intelligent agents that leverage generative capabilities for reasoning, planning, and executing complex tasks autonomously. The ideal candidate will bridge the gap between generative AI's creative potential and agentic AI's autonomous action, developing systems that can understand, reason, and act in dynamic environments.

Key Responsibilities

Integrated AI System Development:

Design and build AI agents that utilize large language models for reasoning and decision-making

Develop systems where generative AI components enable sophisticated planning and problem-solving

Create autonomous agents capable of using tools, APIs, and external systems through generative interfaces

Implement multi-agent systems where generative AI facilitates communication and collaboration

Generative AI Capabilities:

Fine-tune and optimize large language models for specific agentic tasks

Develop prompt engineering strategies for complex reasoning and chain-of-thought processes

Implement RAG (Retrieval-Augmented Generation) systems to enhance agent knowledge and context

Create generative models for code generation, content creation, and strategic planning within agent frameworks

Agent Architecture & Autonomy:

Build reflective agents that can critique and improve their own reasoning processes

Design goal-oriented systems that use generative AI for planning and adaptation

Implement memory architectures that allow agents to learn from experience and maintain context

Develop safety mechanisms and oversight for autonomous generative agents

Multi-Modal Agent Systems:

Integrate vision, language, and action capabilities within agent frameworks

Develop agents that can process and generate across multiple modalities (text, image, audio)

Create embodied agents that interact with digital and physical environments

Research & Innovation: Stay current with the latest academic research and open-source advancements in generative AI. Prototype new ideas and conduct experiments to validate their feasibility and impact.

Education: Ph.D in Computer Science, Electrical Engineering, Mechanical Engineering or related streams.

Technical Proficiency:

Experience with generative AI (LLMs, diffusion models, generative architectures)

Experience with agentic AI systems, reinforcement learning, or autonomous systems

Strong programming skills in Python and experience with AI/ML frameworks (PyTorch, TensorFlow)

Experience with LangChain, AutoGPT, Microsoft Autogen, or similar agent frameworks

Proficiency with transformer architectures and fine-tuning techniques

Deep understanding of prompt engineering, reasoning techniques, and LLM capabilities

Experience with RAG systems, vector databases, and knowledge retrieval

Knowledge of reinforcement learning, planning algorithms, and decision-making systems

Familiarity with multi-agent systems and emergent behavior

Ph.D

What Tata Consultancy Services employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

Apply