AI Data Engineer
Detroit, MI · On-site
$113K - $136K/yr
Support AI model lifecycle: Collaborate with data scientists and ML engineers to prepare, integrate, and manage large-scale datasets for model training and deployment. * Manage real-time data:
AI Data Engineer
Detroit, MI · On-site
$113K - $136K/yr
Support AI model lifecycle: Collaborate with data scientists and ML engineers to prepare, integrate, and manage large-scale datasets for model training and deployment. * Manage real-time data:
AI Trainer - Microbiology Expert
Warren, MI · Remote
$70 - $90/hr
Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required -- your domain knowledge is what matters. Key Responsibilities:
AI Trainer - Microbiology Expert
Warren, MI · Remote
$70 - $90/hr
Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required -- your domain knowledge is what matters. Key Responsibilities:
AI Trainer - Microbiology Expert
Grand Rapids, MI · Remote
$70 - $90/hr
Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required -- your domain knowledge is what matters. Key Responsibilities:
AI Trainer - Microbiology Expert
Grand Rapids, MI · Remote
$70 - $90/hr
Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required -- your domain knowledge is what matters. Key Responsibilities:
AI Trainer - Microbiology Expert
Lansing, MI · Remote
$70 - $90/hr
Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required -- your domain knowledge is what matters. Key Responsibilities:
AI Trainer - Microbiology Expert
Lansing, MI · Remote
$70 - $90/hr
Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required -- your domain knowledge is what matters. Key Responsibilities:
AI Trainer - Microbiology Expert
Sterling Heights, MI · Remote
$70 - $90/hr
Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required -- your domain knowledge is what matters. Key Responsibilities:
AI Trainer - Microbiology Expert
Sterling Heights, MI · Remote
$70 - $90/hr
Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required -- your domain knowledge is what matters. Key Responsibilities:
AI Trainer - Microbiology Expert
Detroit, MI · Remote
$70 - $90/hr
Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required -- your domain knowledge is what matters. Key Responsibilities:
AI Trainer - Microbiology Expert
Detroit, MI · Remote
$70 - $90/hr
Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required -- your domain knowledge is what matters. Key Responsibilities:
AI Trainer - Microbiology Expert
Dearborn, MI · Remote
$70 - $90/hr
Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required -- your domain knowledge is what matters. Key Responsibilities:
AI Trainer - Microbiology Expert
Dearborn, MI · Remote
$70 - $90/hr
Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required -- your domain knowledge is what matters. Key Responsibilities:
AI Trainer - Microbiology Expert
Ann Arbor, MI · Remote
$70 - $90/hr
Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required -- your domain knowledge is what matters. Key Responsibilities:
AI Trainer - Microbiology Expert
Ann Arbor, MI · Remote
$70 - $90/hr
Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required -- your domain knowledge is what matters. Key Responsibilities:
Ensure every model update is tested before it reaches a vehicle. Automotive AI Benchmarks: Build and curate evaluation datasets and benchmarks purpose-built for automotive AI use cases - voice ...
Ensure every model update is tested before it reaches a vehicle. Automotive AI Benchmarks: Build and curate evaluation datasets and benchmarks purpose-built for automotive AI use cases - voice ...
Ensure every model update is tested before it reaches a vehicle. Automotive AI Benchmarks: Build and curate evaluation datasets and benchmarks purpose-built for automotive AI use cases -- voice ...
Ensure every model update is tested before it reaches a vehicle. Automotive AI Benchmarks: Build and curate evaluation datasets and benchmarks purpose-built for automotive AI use cases -- voice ...
Ensure every model update is tested before it reaches a vehicle. Automotive AI Benchmarks: Build and curate evaluation datasets and benchmarks purpose-built for automotive AI use cases -- voice ...
Ensure every model update is tested before it reaches a vehicle. Automotive AI Benchmarks: Build and curate evaluation datasets and benchmarks purpose-built for automotive AI use cases -- voice ...
Ensure every model update is tested before it reaches a vehicle. Automotive AI Benchmarks: Build and curate evaluation datasets and benchmarks purpose-built for automotive AI use cases -- voice ...
Ensure every model update is tested before it reaches a vehicle. Automotive AI Benchmarks: Build and curate evaluation datasets and benchmarks purpose-built for automotive AI use cases -- voice ...
Ensure every model update is tested before it reaches a vehicle. * Automotive AI Benchmarks: Build and curate evaluation datasets and benchmarks purpose-built for automotive AI use cases -- voice ...
Ensure every model update is tested before it reaches a vehicle. * Automotive AI Benchmarks: Build and curate evaluation datasets and benchmarks purpose-built for automotive AI use cases -- voice ...
Ensure every model update is tested before it reaches a vehicle. * Automotive AI Benchmarks: Build and curate evaluation datasets and benchmarks purpose-built for automotive AI use cases -- voice ...
Ensure every model update is tested before it reaches a vehicle. * Automotive AI Benchmarks: Build and curate evaluation datasets and benchmarks purpose-built for automotive AI use cases -- voice ...
Ensure every model update is tested before it reaches a vehicle. Automotive AI Benchmarks: Build and curate evaluation datasets and benchmarks purpose-built for automotive AI use cases -- voice ...
Ensure every model update is tested before it reaches a vehicle. Automotive AI Benchmarks: Build and curate evaluation datasets and benchmarks purpose-built for automotive AI use cases -- voice ...
Generative AI Model Implementation: * Develop, fine-tune, and deploy Generative AI models (e.g., GPT, Stable Diffusion, DALL-E, etc.) for automation tasks. * Integrate pre-trained models or build ...
Generative AI Model Implementation: * Develop, fine-tune, and deploy Generative AI models (e.g., GPT, Stable Diffusion, DALL-E, etc.) for automation tasks. * Integrate pre-trained models or build ...
Generative AI Model Implementation: * Develop, fine-tune, and deploy Generative AI models (e.g., GPT, Stable Diffusion, DALL-E, etc.) for automation tasks. * Integrate pre-trained models or build ...
Generative AI Model Implementation: * Develop, fine-tune, and deploy Generative AI models (e.g., GPT, Stable Diffusion, DALL-E, etc.) for automation tasks. * Integrate pre-trained models or build ...
Generative AI Model Implementation: * Develop, fine-tune, and deploy Generative AI models (e.g., GPT, Stable Diffusion, DALL-E, etc.) for automation tasks. * Integrate pre-trained models or build ...
Generative AI Model Implementation: * Develop, fine-tune, and deploy Generative AI models (e.g., GPT, Stable Diffusion, DALL-E, etc.) for automation tasks. * Integrate pre-trained models or build ...
Generative AI Model Implementation: * Develop, fine-tune, and deploy Generative AI models (e.g., GPT, Stable Diffusion, DALL-E, etc.) for automation tasks. * Integrate pre-trained models or build ...
Generative AI Model Implementation: * Develop, fine-tune, and deploy Generative AI models (e.g., GPT, Stable Diffusion, DALL-E, etc.) for automation tasks. * Integrate pre-trained models or build ...
Generative AI Model Implementation: * Develop, fine-tune, and deploy Generative AI models (e.g., GPT, Stable Diffusion, DALL-E, etc.) for automation tasks. * Integrate pre-trained models or build ...
Generative AI Model Implementation: * Develop, fine-tune, and deploy Generative AI models (e.g., GPT, Stable Diffusion, DALL-E, etc.) for automation tasks. * Integrate pre-trained models or build ...
Ai Model information
What is the difference between Ai Model vs Data Scientist?
| Aspect | Ai Model | Data Scientist |
|---|---|---|
| Required Credentials | Knowledge of machine learning, programming skills, sometimes certifications in AI/ML | Degree in data science, statistics, computer science; certifications beneficial |
| Work Environment | Focus on developing, training, and deploying AI models | Data analysis, interpretation, and visualization; often collaborates with AI teams |
| Industry Usage | Used in AI development, automation, and predictive modeling | Applied across industries for insights, reporting, and decision-making |
While both roles involve working with data and algorithms, an Ai Model primarily focuses on creating and refining AI systems, whereas a Data Scientist analyzes data to generate insights and supports AI development. The roles often overlap but serve distinct functions within the data and AI ecosystem.
What are some common challenges faced by professionals working as AI model developers, and how can they address them?
Professionals working as AI Model developers often encounter challenges such as managing large and complex datasets, ensuring model accuracy, and addressing issues of bias in algorithms. They may also need to balance the trade-off between model performance and interpretability, especially when deploying models in production environments. To overcome these challenges, AI Model developers typically collaborate closely with data engineers, domain experts, and other stakeholders, regularly validate their models, and stay updated with the latest advancements in the field to adopt best practices.
What is an AI model?
AI models are computer programs designed to simulate human intelligence by learning patterns from data and making predictions or decisions based on that learning. These models can perform a variety of tasks, such as recognizing speech, translating languages, analyzing images, and generating text. AI models are created using machine learning algorithms and are trained on large datasets to improve their accuracy and performance. Popular examples include neural networks, decision trees, and support vector machines. The effectiveness of an AI model depends on the quality of the data, the chosen algorithm, and the training process.
What are the key skills and qualifications needed to thrive as an AI model, and why are they important?
To excel as an AI Model Developer, you need strong programming skills (especially in Python), a solid understanding of machine learning algorithms, and typically a degree in computer science, data science, or a related field. Familiarity with ML frameworks like TensorFlow or PyTorch, cloud platforms, and relevant certifications such as TensorFlow Developer or AWS Machine Learning Specialty are valuable. Critical thinking, continuous learning, and effective collaboration with interdisciplinary teams are key soft skills for success. These competencies enable the creation of accurate, reliable AI models that can effectively solve complex real-world problems.
How to get into AI modeling?
To become an AI modeler, develop strong skills in programming languages like Python, learn machine learning frameworks such as TensorFlow or PyTorch, and gain experience with data preprocessing and model training. A background in computer science, mathematics, or related fields, along with relevant certifications or courses, can also improve your prospects.
What are popular job titles related to Ai Model jobs in Michigan? For Ai Model jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Ai Model jobs in Michigan look for? The top searched job categories for Ai Model jobs in Michigan are:
What cities in Michigan are hiring for Ai Model jobs? Cities in Michigan with the most Ai Model job openings:
Job description
Job Description:
We are seeking an experienced and highly skilled AI Data Engineer to join our team. The successful candidate will be responsible for designing, building, and maintaining the data infrastructure and pipelines that power our AI, machine learning (ML), agentic AI, and generative AI (GenAI) initiatives. This role requires strong expertise in data engineering best practices and a deep understanding of the unique data needs of AI models.
Key responsibilities
- Build AI-ready data pipelines: Design, construct, and optimize scalable Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) pipelines specifically for AI and ML models.
- Architect data solutions: Develop and manage data architectures, including data lakes, data warehouses, and vector databases, to support various AI workloads.
- Ensure data quality and governance: Implement data validation, security, and governance policies to ensure the integrity, accessibility, and compliance of data used in AI models.
- Support AI model lifecycle: Collaborate with data scientists and ML engineers to prepare, integrate, and manage large-scale datasets for model training and deployment.
- Manage real-time data: Develop streaming data pipelines using technologies like Apache Kafka to support real-time AI applications and analytics.
- Optimize cloud infrastructure: Utilize AWS cloud computing platforms to build, deploy, and scale AI data solutions efficiently.
- Deploy AI models: Automate the training and deployment of AI/ML models into production via APIs and microservices.
- Monitor and troubleshoot: Implement data observability tools to monitor pipeline health, identify data drift, and quickly resolve any data quality issues that may impact model performance.
- AI-assisted development: Use AI assistants like Copilot in Microsoft Fabric notebooks to generate, explain, and fix code, accelerate data analysis, and streamline data transformation tasks.
Required qualifications
- Education: A Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related technical field is typically required.
- Experience: Proven experience in a data engineering or similar role, with specific experience supporting AI and ML projects.
- Programming: Fluency in programming languages such as Python and SQL, and familiarity with others like Java or Scala.
- Frameworks: Hands-on experience with ML frameworks like TensorFlow, PyTorch, and Scikit-learn, as well as LLM-specific tools like LangChain or LlamaIndex.
- Big data: Experience with distributed data processing frameworks such as Apache Spark and Hadoop.
- Cloud platforms: Proficiency with at least one major cloud provider (AWS, Azure, or GCP) and its AI data-related services.
- Databases: 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 MLflow is highly valued.
Job Description:
We are seeking an experienced and highly skilled AI Data Engineer to join our team. The successful candidate will be responsible for designing, building, and maintaining the data infrastructure and pipelines that power our AI, machine learning (ML), agentic AI, and generative AI (GenAI) initiatives. This role requires strong expertise in data engineering best practices and a deep understanding of the unique data needs of AI models.
Key responsibilities
- Build AI-ready data pipelines: Design, construct, and optimize scalable Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) pipelines specifically for AI and ML models.
- Architect data solutions: Develop and manage data architectures, including data lakes, data warehouses, and vector databases, to support various AI workloads.
- Ensure data quality and governance: Implement data validation, security, and governance policies to ensure the integrity, accessibility, and compliance of data used in AI models.
- Support AI model lifecycle: Collaborate with data scientists and ML engineers to prepare, integrate, and manage large-scale datasets for model training and deployment.
- Manage real-time data: Develop streaming data pipelines using technologies like Apache Kafka to support real-time AI applications and analytics.
- Optimize cloud infrastructure: Utilize AWS cloud computing platforms to build, deploy, and scale AI data solutions efficiently.
- Deploy AI models: Automate the training and deployment of AI/ML models into production via APIs and microservices.
- Monitor and troubleshoot: Implement data observability tools to monitor pipeline health, identify data drift, and quickly resolve any data quality issues that may impact model performance.
- AI-assisted development: Use AI assistants like Copilot in Microsoft Fabric notebooks to generate, explain, and fix code, accelerate data analysis, and streamline data transformation tasks.
Required qualifications
- Education: A Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related technical field is typically required.
- Experience: Proven experience in a data engineering or similar role, with specific experience supporting AI and ML projects.
- Programming: Fluency in programming languages such as Python and SQL, and familiarity with others like Java or Scala.
- Frameworks: Hands-on experience with ML frameworks like TensorFlow, PyTorch, and Scikit-learn, as well as LLM-specific tools like LangChain or LlamaIndex.
- Big data: Experience with distributed data processing frameworks such as Apache Spark and Hadoop.
- Cloud platforms: Proficiency with at least one major cloud provider (AWS, Azure, or GCP) and its AI data-related services.
- Databases: 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 MLflow is highly valued.
About IntraEdge
Sourced by ZipRecruiter
At heart, we are a technology, products and services organization In our soul, it’s the people who make us what we are — the professionals we train and connect to next-level opportunities and the experts who create innovative solutions and value for our national and international partners. It’s true that innovative technology can provide a major boost to your business, but you also need the right talent pushing it forward. This critical combination is what we offer all of our partners: cutting edge tech solutions and the expertise to bring it to life.
Industry
It services
Company size
1,001 - 5,000 Employees
Headquarters location
Chandler, AZ, US
Year founded
2002