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Genai Engineer Jobs in Michigan (NOW HIRING)

Deloitte

Delivery Management Engineer III

Detroit, MI · Hybrid

You will work directly with engineering teams, assisting with product and service delivery for AI, GenAI, and Cloud platform programs. You'll have full life-cycle project experience with specific ...

Stefanini Group

GCP Data Engineer

Dearborn, MI · On-site

Partner with AI/ML teams to integrate ML, statistical models, and GenAI into data platform ... Advise engineering, data, and AI teams on best practices. * Foster an AI-assisted, platform-first ...

Stefanini Group

GCP Data Engineer

Dearborn, MI · On-site

Partner with AI/ML teams to integrate ML, statistical models, and GenAI into data platform ... Advise engineering, data, and AI teams on best practices. * Foster an AI-assisted, platform-first ...

Stefanini Group

GCP Data Engineer

Dearborn, MI · On-site

$61 - $66/hr

Partner with AI/ML teams to integrate ML, statistical models, and GenAI into data platform ... Advise engineering, data, and AI teams on best practices. * Foster an AI-assisted, platform-first ...

Stefanini Group

GCP Data Engineer

Dearborn, MI

Partner with AI/ML teams to integrate ML, statistical models, and GenAI into data platform ... Advise engineering, data, and AI teams on best practices. * Foster an AI-assisted, platform-first ...

Pwc

AI & GenAI Data Scientist - Manager

Grand Rapids, MI · On-site

$99K - $232K/yr

... 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 ...

Deloitte

Delivery Management Engineer II

Detroit, MI · Hybrid

$55.25 - $73.75/hr

You will work directly with engineering teams, assisting with product and service delivery for AI, GenAI, and Cloud platform programs. You'll have full life-cycle project experience with specific ...

Pwc

AI & GenAI Data Scientist - Manager

Detroit, MI · On-site

$99K - $232K/yr

... 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 ...

Deloitte

Senior AI Engineer - SFL Scientific

Detroit, MI

$103K - $142K/yr

Adopt best engineering practices in automation, HPC and AI/GenAI infrastructure and design patterns * Define and lead technology proof of concepts to ensure feasibility of new data and cloud ...

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Genai Engineer information

What is a GenAI engineer?

A GenAI Engineer is a professional who specializes in designing, developing, and deploying generative artificial intelligence (AI) models and applications. This role involves working with advanced machine learning techniques, such as large language models and generative adversarial networks, to create systems that can generate text, images, code, or other content. GenAI Engineers collaborate with data scientists, software engineers, and product teams to integrate AI capabilities into products and services, ensuring ethical use and scalability. They also stay updated on the latest developments in AI research to continually improve model performance and effectiveness.

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

To thrive as a GenAI Engineer, you need expertise in machine learning, deep learning, and programming languages such as Python, along with a solid understanding of generative models like GANs and transformers. Familiarity with frameworks such as TensorFlow or PyTorch, and experience with cloud platforms and MLOps tools, are highly valuable; advanced degrees or certifications in AI or data science are often preferred. Strong problem-solving, creativity, and communication skills help GenAI Engineers design innovative solutions and effectively collaborate with multidisciplinary teams. These skills ensure the development of robust, scalable generative AI systems that address complex real-world challenges.

What are some typical challenges a GenAI engineer faces when deploying AI models in production environments?

GenAI Engineers often encounter challenges such as ensuring model scalability, addressing bias in generated outputs, and maintaining performance consistency in real-world applications. Deploying generative AI models requires careful monitoring to prevent unexpected or inappropriate outputs, as well as efficient resource management to handle large-scale computations. Collaborating closely with data engineers, product managers, and ML operations teams is essential to streamline deployment pipelines and quickly resolve issues that arise in live environments.

What is the difference between Genai Engineer vs Data Scientist?

AspectGenai EngineerData Scientist
Required CredentialsDegree in Computer Science, AI, or related fields; experience with AI/ML frameworksDegree in Data Science, Statistics, or related fields; strong programming skills
Work EnvironmentDevelops AI models, fine-tunes generative AI systems, collaborates with AI teamsAnalyzes data, builds predictive models, interprets complex datasets
Employer & Industry UsageTech companies, AI startups, research labs focusing on generative AIFinance, healthcare, marketing, and tech firms analyzing data for insights

While both roles require strong technical skills and a background in data or AI, Genai Engineers focus on developing and deploying generative AI models, whereas Data Scientists analyze data to extract insights and build predictive models. The roles often overlap but serve different primary functions within AI and data-driven organizations.

What are popular job titles related to Genai Engineer jobs in Michigan?

For Genai Engineer jobs in Michigan, the most frequently searched job titles are:

What job categories do people searching Genai Engineer jobs in Michigan look for?

The top searched job categories for Genai Engineer jobs in Michigan are:

What cities in Michigan are hiring for Genai Engineer jobs?

Cities in Michigan with the most Genai Engineer job openings:

Genai Engineer jobs near you
Infographic showing various Genai Engineer job openings in Michigan as of August 2026, with employment types broken down into 87% Full Time, 6% Part Time, 2% Temporary, 4% Contract, and 1% Nights. Highlights an 85% Physical, 6% Hybrid, and 9% Remote job distribution.

Principal Engineer - Supply Chain AI Solutions

Harman International Industries

Novi, MI • On-site

Full-time

Re-posted 12 days ago

Key responsibilities

  • Drive hands-on delivery of AI and Generative AI solutions that streamline supply chain workflows and deliver measurable business value.

  • Architect, develop, and maintain production-grade systems including RAG pipelines, agentic tools, model routing, vector search, evaluation, guardrails, and observability, integrated with internal platforms and enterprise datasets.

  • Own the complete solution lifecycle from problem definition and prototyping to deployment, monitoring, and continuous improvement.

Job description

A Career at HARMAN
As a technology leader that is rapidly on the move, HARMAN is filled with people who are focused on making life better. Innovation, inclusivity and teamwork are a part of our DNA. When you add that to the challenges we take on and solve together, you'll discover that at HARMAN you can grow, make a difference and be proud of the work you do every day.
Introduction: A Career at HARMAN Automotive
We're a global, multi-disciplinary team that's putting the innovative power of technology to work and transforming tomorrow. At HARMAN Automotive, we give you the keys to fast-track your career.
  • Engineer audio systems and integrated technology platforms that augment the driving experience
  • Combine ingenuity, in-depth research, and a spirit of collaboration with design and engineering excellence
  • Advance in-vehicle infotainment, safety, efficiency, and enjoyment

About the Role
Drive hands-on delivery of AI and Generative AI solutions that streamline supply chain workflows and deliver measurable business value through hours saved, cycle-time reduction, improved decision quality, risk mitigation, and the breadth of users served. You will architect, develop, and maintain production-grade systems encompassing RAG pipelines, agentic tools, model routing, vector search, evaluation and guardrails, and observability, all tightly integrated with internal platforms, enterprise datasets, and supply chain systems. This is primarily a hands-on GenAI and software engineering role, with supply chain expertise providing the domain context for solution design and delivery.
What You Will Do
  • Automate high-impact supply chain workflows for internal stakeholders, prioritizing initiatives with the greatest time savings, business impact, and user reach.
  • Deliver production-ready copilots and applications for knowledge search, document summarization, intelligent recommendations, conversational analytics, exception management, and end-to-end workflow automation.
  • Apply GenAI and software engineering to supply chain use cases across procurement; supplier collaboration and management; risk management; quality; costing; engineering; materials and warehouse management; finished-goods and component-level planning; and ESG.
  • Architect and develop scalable, high-performance data and AI systems that support RAG, agentic workflows, secure tool use, and model orchestration.
  • Own the complete solution lifecycle, from problem definition and rapid prototyping through rigorous evaluation, production deployment, ongoing monitoring, and continuous improvement.
  • Design and implement RAG pipelines over heterogeneous and often messy enterprise and supply chain data, including contracts, purchase orders, supplier documents, bills of material, requirements, quality records, audit artifacts, planning data, business rules, and unstructured content. Select embedding strategies, chunking approaches, vector search configurations, rerankers, metadata or knowledge-graph enrichment techniques, and routing policies to maximize retrieval quality.
  • Develop agentic workflows leveraging LangChain, LlamaIndex, Model Context Protocol (MCP), and agent-to-agent (A2A) protocols; build secure tools that allow agents to retrieve data and execute approved actions in enterprise systems.
  • Integrate AI solutions with enterprise applications and data platforms through APIs, events, batch pipelines, and governed access patterns; design integrations that are resilient, observable, and maintainable.
  • Evaluate when to use platform-native embedded AI capabilities versus custom-built GenAI components, and design modular solutions that can evolve with the enterprise tool landscape.
  • Translate subject-matter-expert knowledge into robust prompts, tools, workflow logic, and validation rules; evaluate trade-offs among prompt engineering, retrieval augmentation, fine-tuning, and deterministic software.
  • Work hands-on with large language models, vector databases such as Pinecone and FAISS, and agent memory systems.
  • Establish operational excellence through rigorous SLAs; safety and guardrail mechanisms; prompt and version management; transparent evaluation; latency and throughput optimization; cost controls; load balancing; fallback or model-routing strategies; and human review for process-critical decisions.
  • Establish observability using tools such as Datadog, Grafana, and LangFuse, along with model and data governance, access controls, auditability, and operational support appropriate for internal enterprise environments.
  • Build and maintain data products, lakes, and warehouses using platforms such as Snowflake, Delta Lake, BigQuery, and Microsoft Fabric to support supply chain AI use cases.
  • Build internal copilots and customer-facing features using React, Node.js, and Python with REST or GraphQL backends; containerize applications with Docker, orchestrate with Kubernetes, automate CI/CD pipelines, and manage infrastructure as code using tools such as Terraform.
  • Collaborate closely with supply chain subject-matter experts, requirements, testing, validation, cybersecurity, data, platform, and enterprise application teams; communicate proactively and iterate rapidly in a fast-paced environment.

What You Need To Be Successful
  • 8+ years of experience building production software, ideally including ML systems and hands-on work with LLMs and Generative AI; demonstrated technical leadership while remaining deeply hands-on.
  • Programming: Python (FastAPI, NumPy, Pandas, scikit-learn, Pydantic, Jinja2) and Node.js; strong proficiency with APIs and distributed systems.
  • LLMs and Frameworks: Hands-on experience with at least one major deep learning or LLM stack, such as PyTorch/Transformers or TensorFlow/Keras, and orchestration frameworks such as LangChain or LlamaIndex.
  • Model Providers: Working familiarity connecting to inference providers and model ecosystems such as AWS Bedrock, OpenAI, Anthropic, Meta/Llama, and Mistral.
  • Data and Storage: SQL and NoSQL databases (PostgreSQL, DynamoDB), Elasticsearch for search and analytics, and vector databases (Pinecone, Weaviate, FAISS, Milvus, pgvector).
  • Cloud and Infrastructure: AWS (S3, EC2, Lambda, CloudWatch, Fargate, EKS/ECS), Azure, GCP, Databricks, Docker, Kubernetes, Terraform, CI/CD, Airflow, and Kafka.
  • Enterprise Integration: Demonstrated experience integrating production software or AI systems with one or more enterprise platforms using APIs, event-driven interfaces, batch or data pipelines, authentication and authorization, and robust error-handling patterns.
  • Operational Excellence: Load balancing, monitoring and alerting (Datadog, Grafana, LangFuse), debugging production issues, evaluation and release discipline, and cost and performance optimization.
  • Preferred Platform Experience - Strong Plus: Experience with one or more of SAP, SAP S/4HANA, SAP Ariba, enterprise resource planning (ERP) platforms, Ivalua, OneStream, Darwin Analytics, SupplyOn, or SAP Integrated Business Planning (IBP). Experience using or extending platform-native AI capabilities is valuable; breadth across every platform is not required.
  • Preferred Supply Chain Domain Experience - Strong Plus: Delivery experience in one or more of procurement, supplier collaboration and management, risk management, quality, costing, engineering, materials and warehouse management, finished-goods or component-level planning, and ESG. Familiarity with automotive supply chain and compliance concepts such as Extended Producer Responsibility (EPR), Process Release Audit (PRA), and Production Part Approval Process (PPAP) is an additional advantage.
  • Soft Skills: Strong communication abilities, product-oriented thinking, effective collaboration with technical and functional subject-matter experts, and the capacity to learn and adapt quickly in a dynamic environment.
  • Education: BS, MS, or PhD in Computer Science, Electrical Engineering, Mathematics, or equivalent professional experience.

What Makes You Eligible
  • Ability to work from an office in Novi, MI, 3+ days per week (hybrid)
  • Successfully complete a background investigation and drug screen as a condition of employment

What We Offer
  • Access to employee discounts on world-class products (JBL, HARMAN Kardon, AKG, and more)
  • Extensive training opportunities through our own HARMAN University
  • Competitive wellness benefits
  • Tuition reimbursement
  • "Be Brilliant" employee recognition and rewards program
  • An inclusive and diverse work environment that fosters and encourages professional and personal development

#Hybrid
#LI-AA1
Salary Ranges:
$ 125,250 - $ 183,700
HARMAN is proud to be an Equal Opportunity / Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.

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