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Mlops Machine Learning Engineer Jobs in Michigan

Epitec

Machine Learning Engineer

Dearborn, MI · On-site

Contract * Job #104271 Machine Learning Engineer Dearborn, MI (HYBRID/ONSITE) W2 CONTRACT ONLY ... Implement MLOps practices (CI/CD, model monitoring, retraining) * Optimize systems for scalability ...

FastTek

Machine Learning Engineer

Dearborn, MI

Machine Learning Engineer #1058742 Position Description: We are seeking an experienced AI Engineer ... This role combines expertise in Data Science, Software Engineering, and MLOps to deliver scalable ...

Canopy

Senior Machine Learning Engineer

Detroit, MI · On-site

$95K - $131K/yr

Senior Machine Learning Engineer As a Senior Machine Learning Engineer within the AI Squad at ... data engineers, and MLOps teams to ensure seamless model integration and delivery. * Perform ...

Saanvi Technologies

Machine Learning Engineer 3

Dearborn, MI · On-site

$105K - $126K/yr

Machine Learning Engineering Engineer 3 Dearborn, MI W2 Position Description: We are seeking an ... This role combines expertise in Data Science, Software Engineering, and MLOps to deliver scalable ...

V2Soft

Machine Learning Engineer

Dearborn, MI · On-site

$105K - $126K/yr

... with MLOps tools and platforms including MLflow, Airflow, Vertex AI, SageMaker, Kubeflow, or ... • Machine Learning & Deep Learning • LLMs • Prompt Engineering • RAG • Embeddings • ...

Stefanini

Machine Learning Engineer

Dearborn, MI

... CT, MLOps, and LLMOps - including guardrail integration, prompt versioning, and observability ... Machine Learning model fine-tuning. Familiarity with data engineering concepts and practices.

Eccalon LLC

Machine Learning Engineer

Detroit, MI · On-site

Machine Learning Engineer Location: Detroit, MI- Onsite Type: Full-time Security Clearance: No clearance required, must be clearable. The Machine Learning Engineer will be an essential member of the ...

Stefanini

Machine Learning Engineer

Dearborn, MI

$105K - $126K/yr

Stefanini is looking for a Machine Learning Engineer (Dearborn, MI) For quick apply, please reach ... MLOps. Committed code to improve open-source data/software engineering projects Experience ...

Mariana Minerals

Machine Learning Engineer

Ann Arbor, MI · On-site

$120K - $160K/yr

As a Machine Learning Engineer at Mariana, you'll help build and improve the machine learning systems that control our mineral refining facilities. You'll start with well-scoped problems inside our ...

Canopy

Senior Machine Learning Engineer

Detroit, MI · On-site +1

$126K - $180K/yr

As a Senior Machine Learning Engineer within the AI Squad at Canopy and reporting to the Director ... data engineers, and MLOps teams to ensure seamless model integration and delivery. * Perform ...

Indeed

Machine Learning Engineer

Detroit, MI

As a Machine Learning Engineer, you will prepare datasets, train and optimize models, and maintain and improve model inference services. You will learn and apply new techniques from open source ...

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Mlops Machine Learning Engineer information

Is MLOps harder than DevOps?

MLOps, as a specialized subset of DevOps focused on deploying and maintaining machine learning models, often involves additional challenges such as data management, model versioning, and monitoring. While both require skills in automation, scripting, and cloud environments, MLOps typically demands expertise in machine learning workflows and tools like TensorFlow or PyTorch, making it more complex in certain aspects compared to traditional DevOps.

What does an MLOps Machine Learning Engineer do?

An MLOps Machine Learning Engineer bridges the gap between data science and IT operations by developing, deploying, and maintaining machine learning models in production environments. They are responsible for automating workflows, managing model versioning, monitoring performance, and ensuring scalability and reliability of ML systems. Their work enables organizations to deploy machine learning solutions efficiently and consistently, making it easier to update and manage models as business needs evolve.

How does an MLOps Machine Learning Engineer typically collaborate with data scientists and software engineers during the deployment of machine learning models?

An MLOps Machine Learning Engineer acts as a bridge between data scientists and software engineers, ensuring machine learning models transition smoothly from development to production. They often work closely with data scientists to understand model requirements, data pipelines, and performance metrics, while also collaborating with software engineers to integrate models into scalable systems. Regular communication, shared documentation, and joint troubleshooting sessions are common, as the role requires aligning model performance with system reliability and maintainability. This collaborative environment helps ensure that models are robust, scalable, and impactful in real-world applications.

What is the difference between Mlops Machine Learning Engineer vs Data Scientist?

AspectMlops Machine Learning EngineerData Scientist
Required CredentialsBachelor's or master's in CS, data science, or related fields; certifications in cloud platforms or MLOps toolsBachelor's or master's in statistics, data science, or related fields; certifications in data analysis or machine learning
Work EnvironmentFocus on deploying, maintaining, and scaling ML models in production environmentsFocus on data analysis, model development, and insights generation
Employer & Industry UsageTech companies, startups, enterprises implementing ML solutionsResearch institutions, analytics firms, tech companies for data insights

While both roles involve machine learning, Mlops Machine Learning Engineers specialize in deploying and maintaining models in production, ensuring scalability and reliability. Data Scientists primarily focus on developing models and analyzing data to generate insights. The roles often overlap but differ in their core responsibilities and work environments.

Are MLOps engineers in demand?

MLOps engineers are in high demand due to the increasing adoption of machine learning models in various industries. Their skills in deploying, managing, and scaling machine learning systems, along with knowledge of tools like Docker, Kubernetes, and cloud platforms, make them valuable in the job market.

What engineers make $500,000?

Senior machine learning engineers, including those specializing in MLOps, often reach or exceed $500,000 annually with experience, advanced skills, and in high-demand industries like tech or finance. Compensation can include base salary, bonuses, and stock options, especially at large tech companies or startups with significant funding.

How much do MLOps engineers make?

MLOps engineers typically earn between $100,000 and $150,000 annually, with salaries increasing based on experience, location, and expertise in tools like Kubernetes, Docker, and cloud platforms. Senior roles or those with specialized skills can exceed $180,000 per year.

What are the key skills and qualifications needed to thrive as an MLOps Machine Learning Engineer, and why are they important?

To thrive as an MLOps Machine Learning Engineer, you need a strong background in machine learning concepts, software engineering, and cloud infrastructure, typically supported by a degree in computer science or a related field. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (AWS, GCP, Azure), and certifications such as Google Professional Machine Learning Engineer are highly beneficial. Strong problem-solving abilities, collaboration, and communication skills help you work effectively across data science and engineering teams. These skills are essential for reliably deploying, monitoring, and maintaining scalable machine learning solutions in production environments.
What are popular job titles related to Mlops Machine Learning Engineer jobs in Michigan? For Mlops Machine Learning Engineer jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Mlops Machine Learning Engineer jobs in Michigan look for? The top searched job categories for Mlops Machine Learning Engineer jobs in Michigan are:
What cities in Michigan are hiring for Mlops Machine Learning Engineer jobs? Cities in Michigan with the most Mlops Machine Learning Engineer job openings:
Mlops Machine Learning Engineer jobs near you
Machine Learning Engineer

Machine Learning Engineer

Epitec

Dearborn, MI • On-site

Contractor

Posted 6 days ago

Job description

  • Location: Dearborn, Michigan
  • Type: Contract
  • Job #104271

Machine Learning Engineer
Dearborn, MI (HYBRID/ONSITE)
W2 CONTRACT ONLY
Overview
We are seeking an experienced AI Engineer to design, build, and deploy scalable AI solutions leveraging Machine Learning, LLMs, and Agentic AI. This role focuses on delivering production-ready systems that drive automation, efficiency, and measurable business impact within a cloud-based environment.
Key Responsibilities
  • Develop and deploy ML models (predictive, optimization, Generative AI)
  • Build end-to-end AI pipelines (data ingestion ? modeling ? deployment ? monitoring)
  • Design LLM-based applications (RAG, prompt orchestration, agentic workflows, tool integrations)
  • Create APIs and AI services for enterprise integration
  • Implement MLOps practices (CI/CD, model monitoring, retraining)
  • Optimize systems for scalability, performance, and reliability
  • Partner with stakeholders to translate business needs into AI solutions
Required Qualifications
  • Bachelor's degree in Computer Science, Data Science, or related field
  • 6+ years IT experience; 4+ years in development
  • Strong Python expertise (APIs, backend, automation)
  • Experience deploying ML models in production
  • Hands-on with LLMs, prompt engineering, Generative AI
  • Experience with AWS or GCP (BigQuery preferred)
  • Proficiency in ML frameworks (Scikit-learn, TensorFlow, PyTorch)
  • Strong understanding of SDLC, testing, and deployment practices
Preferred / Nice to Have
  • Experience with Agentic AI, autonomous agents, tool-calling architectures
  • Familiarity with LangChain, LlamaIndex, CrewAI, AutoGen
  • MLOps tools: MLflow, Airflow, Vertex AI, SageMaker, Kubeflow
  • Docker, Kubernetes, and cloud-native architecture
  • Knowledge of vector databases, embeddings, RAG, semantic search
  • Experience with large-scale data environments, ETL pipelines, data lakes
Core Skills
  • Python, SQL
  • Machine Learning & Deep Learning
  • LLMs, RAG, Prompt Engineering, Embeddings
  • Agentic AI / AI Agents
  • Cloud (AWS or GCP)
  • API Development (FastAPI / Flask)
  • MLOps, CI/CD, Model Deployment & Monitoring

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