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Genai Developer Jobs in Michigan (NOW HIRING)

... GenAI Data Scientist - Manager, you will play a pivotal role in transforming raw data into ... In this role at PwC, you will apply data, algorithms, and software engineering to build and deploy ...

... GenAI Data Scientist - Manager, you will play a pivotal role in transforming raw data into ... In this role at PwC, you will apply data, algorithms, and software engineering to build and deploy ...

We are hiring an AI Engineer to build and operate the data, features, and GenAI foundations that power Human Capital AI products and analytics. You will work with an AI Data Engineer (data ingestion ...

GCP Data Engineer

Dearborn, MI · On-site

$84.03 - $90.92/hr

Partner with AI/ML teams to integrate ML, statistical models, and GenAI into data platform ... Experience building Internal Developer Platforms (IDPs). * GCP Professional Cloud Database Engineer ...

Senior Forward Deployed Engineer- AWS

Detroit, MI · On-site

$103K - $142K/yr

Embed with clients to identify business needs and translate high-value GenAI use cases into solutions. * Partner with leaders, product owners, architects, and engineers to align priorities and ...

Lead Forward Deployed Engineer - AWS

Detroit, MI · On-site

$101K - $133K/yr

Embed with clients to identify business needs and translate high-value GenAI use cases into solutions. * Partner with leaders, product owners, architects, and engineers to align priorities and ...

AI Data Engineer

Detroit, MI · On-site

$113K - $136K/yr

Expertise in both relational (SQL) and NoSQL databases, including vector databases for GenAI applications. * DevOps and MLOps: Experience with CI/CD, Docker, and ML lifecycle management tools like ...

Showing results 21-40

Genai Developer information

See Michigan salary details

$14

$46

$71

How much do genai developer jobs pay per hour?

As of Sep 4, 2026, the average hourly pay for genai developer in Michigan is $46.05, according to ZipRecruiter salary data. Most workers in this role earn between $35.19 and $56.35 per hour, depending on experience, location, and employer.

What is a GenAI developer?

GenAI Developers are professionals who design, build, and optimize applications using generative artificial intelligence technologies. They work with models such as GPT, DALL-E, or Stable Diffusion to create tools for generating text, images, code, and other content. These developers need strong programming skills, a solid understanding of machine learning, and experience working with AI frameworks and APIs. Their responsibilities often include training custom models, integrating AI into products, and ensuring ethical use of generative AI solutions.

What are some common challenges GenAI developers face when integrating generative AI models into existing products?

GenAI Developers often encounter challenges related to model deployment, scalability, and ensuring data privacy when integrating generative AI models into established products. Balancing the computational requirements of large AI models with real-time application demands can be complex, and optimizing inference speed without sacrificing model quality is a key consideration. Additionally, collaborating closely with product managers, data scientists, and DevOps teams is essential to align AI outputs with business goals and maintain robust, ethical AI practices.

What are the key skills and qualifications needed to thrive as a GenAI developer, and why are they important?

To thrive as a GenAI Developer, you need a strong background in machine learning, deep learning frameworks (like TensorFlow or PyTorch), and programming languages such as Python, often supported by a degree in computer science or a related field. Familiarity with cloud platforms (AWS, Azure, GCP), APIs, and prompt engineering, as well as certifications in AI or ML, are typically used in this role. Creativity, problem-solving, and effective communication set outstanding GenAI Developers apart. These skills are crucial for building, optimizing, and deploying powerful generative AI models that address complex business challenges.

What is the difference between Genai Developer vs Machine Learning Engineer?

AspectGenai DeveloperMachine Learning Engineer
Required CredentialsBachelor's in CS, AI, or related; experience with NLP and AI frameworksBachelor's or higher in CS, Data Science, or related; strong programming and ML skills
Work EnvironmentDevelops AI models focused on generative AI, often in AI startups or tech companiesBuilds and deploys ML models across various industries, including tech, finance, healthcare
Employer & Industry UsagePrimarily in AI-focused companies, research labs, and tech firmsWidely used across industries like tech, finance, healthcare, and retail

While both roles involve AI and machine learning, Genai Developers specialize in creating generative AI models like chatbots and content generators, whereas Machine Learning Engineers develop a broader range of ML models for various applications. The roles overlap in skills and tools but differ in focus and industry applications.

How to become a GenAI developer?

To become a GenAI developer, you should gain expertise in machine learning, deep learning, and natural language processing, with a focus on generative models like GPT. Proficiency in programming languages such as Python, experience with frameworks like TensorFlow or PyTorch, and understanding of large language models are essential. Building a portfolio of projects and staying updated with AI research can also enhance your qualifications.

Is a Genai Developer a promising career?

A Genai Developer is a growing role focused on developing and implementing generative AI models, which are increasingly used across industries. The field requires skills in machine learning, programming, and AI frameworks, and offers strong job growth prospects due to expanding AI adoption. Continuous learning and staying updated with new tools are important for success in this career.
Infographic showing various Genai Developer job openings in Michigan as of August 2026, with employment types broken down into 77% Full Time, 6% Part Time, 2% Temporary, 14% Contract, and 1% Nights. Highlights an 80% Physical, 6% Hybrid, and 14% Remote job distribution, with an average salary of $95,792 per year, or $46.1 per hour.

Lead Forward Deployed Engineer, Palantir

Deloitte

Detroit, MI • On-site

$101K - $133K/yr

Full-time

Re-posted 26 days ago


Deloitte rating

8.2

Company rating: 8.2 out of 10

Based on 93 frontline employees who took The Breakroom Quiz

46th of 152 rated financial services


Job description

At Deloitte, Forward Deployed Engineers (FDE) don't just build AI solutions, they help clients turn AI ambition into enterprise-scale impact, pairing leading class engineering with pod-based delivery and vertical expertise. If you thrive at the intersection of product, engineering, problem-solving, and client impact, this role puts you at the forefront of AI transformations.

Recruiting for this role ends on 10/30/2026.

Work you'll do

As a Lead Palantir FDE, you will serve as the senior practitioner-leader embedded directly with our most strategic clients, leading forward-deployed engineering pods that develop and deploy GenAI solutions into production for Deloitte's most strategic clients. You'll set technical direction, remove delivery blockers, and stay hands-on; designing, reviewing, and debugging systems with the team. You'll translate engineering trade-offs into clear decisions for client leaders when needed. Your ability to influence decisions at the C-suite level, while maintaining hands-on technical credibility, is what sets you apart. Pods under your leadership may be deployed onshore with clients or in hybrid onshore/offshore configurations, leveraging Deloitte's global delivery capability to maximize speed and scale.

Client Engagement

  • Serve as the senior client-facing presence, building trusted advisor relationships as the senior engineering partner for client product, data, and platform leaders
  • Lead executive-level discovery, define success metrics (quality, latency, cost, adoption, risk) and a phased plan from prototype to production and scaling
  • Navigate organizational complexity and influence to align executive sponsors, IT leadership, and business owners around a shared vision
  • Represent Deloitte's FDE capability in client pursuits, executive briefings, and platform partner engagements-contributing to pipeline development and deal shaping.

Cross-Functional Pod Leadership & Program Governance

  • Lead FDE pods of 2-5 onshore anchored and offshore supported engineers, owning execution, resource management, escalations and overall delivery health
  • Enforce delivery standards across the pod: sprint cadences, stakeholder communication plans, risk management, and quality gates
  • Coordinate multi-pod or multi-workstream engagements, ensuring reliable architecture and consistent client experience.
  • Mentor and develop junior FDEs

GenAI Solution Development

  • Architect and oversee delivery of LLM-enabled applications including copilots, agentic workflows, assistants, and knowledge search experiences using one or more enterprise AI platforms (see Platform Requirements below)
  • Set direction for prompt engineering, tool-use patterns, and human-in-the-loop controls
  • Govern end-to-end RAG pipeline design-including ingestion, chunking, embedding, vector retrieval, and hybrid search-ensuring production-grade quality and scalability.
  • Define evaluation frameworks covering quality, hallucination risk, safety, latency, cost, and governance; ensure the pod meets agreed engineering quality bars to these standards.

Engineering & Data Foundations

  • Review and contribute to production-quality code
  • Guide architecture of data pipelines powering GenAI use cases
  • Enforce strong data management, testing, CI/CD, logging, versioning, and documentation practices
  • Deep familiarity with cloud environments (AWS, Azure, and/or Google Cloud)


The team

AI & Engineering leverages cutting-edge engineering capabilities to build, deploy, and operate integrated/verticalized sector solutions in software, data, AI, network, and hybrid cloud infrastructure. These solutions are powered by engineering for business advantage, transforming mission-critical operations. We enable clients to stay ahead with the latest advancements by transforming engineering teams and modernizing technology & data platforms. Our delivery models are tailored to meet each client's unique requirements.

Required qualifications 

  • Bachelor's degree (or equivalent) in Computer Science, Data Science or Engineering
  • 7+ years of experience in software engineering, data engineering, data science, or analytics engineering
  • 1+ years of hands-on experience building and deploying GenAI/LLM-powered solutions in client or production environments
  • 1+ years of experience with Palantir including hands-on experience with one of the following key platforms; Foundry, AIP, Maven 
  • 1+ years of experience leading project workstreams/engagements and translating business problems into AI solutions
  • 1+ years of experience building reliable, maintainable, and well-documented code 
  • Ability to travel 50%, on average, based on the work you do and the clients and industries/sectors you serve
  • Limited immigration sponsorship may be available

Preferred qualifications

  • Experience with cloud environments (AWS, Azure, and/or Google Cloud) and common platform services (storage, compute, IAM, networking)
  • Demonstrated ability to work directly alongside client technical teams and program stakeholders in fast-paced, ambiguous delivery environments 
  • Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation
  • Experience with MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt management 
  • Experience integrating LLM solutions with enterprise systems via APIs, microservices, or event-driven architectures 
  • Experience operating within hybrid onshore/offshore teams 
  • Familiarity with security, privacy, and compliance considerations

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $189,200 to $372,900.

You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.

Qualifications:

At Deloitte, Forward Deployed Engineers (FDE) don't just build AI solutions, they help clients turn AI ambition into enterprise-scale impact, pairing leading class engineering with pod-based delivery and vertical expertise. If you thrive at the intersection of product, engineering, problem-solving, and client impact, this role puts you at the forefront of AI transformations.

Recruiting for this role ends on 10/30/2026.

Work you'll do

As a Lead Palantir FDE, you will serve as the senior practitioner-leader embedded directly with our most strategic clients, leading forward-deployed engineering pods that develop and deploy GenAI solutions into production for Deloitte's most strategic clients. You'll set technical direction, remove delivery blockers, and stay hands-on; designing, reviewing, and debugging systems with the team. You'll translate engineering trade-offs into clear decisions for client leaders when needed. Your ability to influence decisions at the C-suite level, while maintaining hands-on technical credibility, is what sets you apart. Pods under your leadership may be deployed onshore with clients or in hybrid onshore/offshore configurations, leveraging Deloitte's global delivery capability to maximize speed and scale.

Client Engagement

  • Serve as the senior client-facing presence, building trusted advisor relationships as the senior engineering partner for client product, data, and platform leaders
  • Lead executive-level discovery, define success metrics (quality, latency, cost, adoption, risk) and a phased plan from prototype to production and scaling
  • Navigate organizational complexity and influence to align executive sponsors, IT leadership, and business owners around a shared vision
  • Represent Deloitte's FDE capability in client pursuits, executive briefings, and platform partner engagements-contributing to pipeline development and deal shaping.

Cross-Functional Pod Leadership & Program Governance

  • Lead FDE pods of 2-5 onshore anchored and offshore supported engineers, owning execution, resource management, escalations and overall delivery health
  • Enforce delivery standards across the pod: sprint cadences, stakeholder communication plans, risk management, and quality gates
  • Coordinate multi-pod or multi-workstream engagements, ensuring reliable architecture and consistent client experience.
  • Mentor and develop junior FDEs

GenAI Solution Development

  • Architect and oversee delivery of LLM-enabled applications including copilots, agentic workflows, assistants, and knowledge search experiences using one or more enterprise AI platforms (see Platform Requirements below)
  • Set direction for prompt engineering, tool-use patterns, and human-in-the-loop controls
  • Govern end-to-end RAG pipeline design-including ingestion, chunking, embedding, vector retrieval, and hybrid search-ensuring production-grade quality and scalability.
  • Define evaluation frameworks covering quality, hallucination risk, safety, latency, cost, and governance; ensure the pod meets agreed engineering quality bars to these standards.

Engineering & Data Foundations

  • Review and contribute to production-quality code
  • Guide architecture of data pipelines powering GenAI use cases
  • Enforce strong data management, testing, CI/CD, logging, versioning, and documentation practices
  • Deep familiarity with cloud environments (AWS, Azure, and/or Google Cloud)


The team

AI & Engineering leverages cutting-edge engineering capabilities to build, deploy, and operate integrated/verticalized sector solutions in software, data, AI, network, and hybrid cloud infrastructure. These solutions are powered by engineering for business advantage, transforming mission-critical operations. We enable clients to stay ahead with the latest advancements by transforming engineering teams and modernizing technology & data platforms. Our delivery models are tailored to meet each client's unique requirements.

Required qualifications 

  • Bachelor's degree (or equivalent) in Computer Science, Data Science or Engineering
  • 7+ years of experience in software engineering, data engineering, data science, or analytics engineering
  • 1+ years of hands-on experience building and deploying GenAI/LLM-powered solutions in client or production environments
  • 1+ years of experience with Palantir including hands-on experience with one of the following key platforms; Foundry, AIP, Maven 
  • 1+ years of experience leading project workstreams/engagements and translating business problems into AI solutions
  • 1+ years of experience building reliable, maintainable, and well-documented code 
  • Ability to travel 50%, on average, based on the work you do and the clients and industries/sectors you serve
  • Limited immigration sponsorship may be available

Preferred qualifications

  • Experience with cloud environments (AWS, Azure, and/or Google Cloud) and common platform services (storage, compute, IAM, networking)
  • Demonstrated ability to work directly alongside client technical teams and program stakeholders in fast-paced, ambiguous delivery environments 
  • Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation
  • Experience with MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt management 
  • Experience integrating LLM solutions with enterprise systems via APIs, microservices, or event-driven architectures 
  • Experience operating within hybrid onshore/offshore teams 
  • Familiarity with security, privacy, and compliance considerations

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $189,200 to $372,900.

You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.

Education:Bachelor's DegreeEmployment Type:

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