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Mlops Machine Learning Engineer Jobs in New Jersey

... an MLOps Engineer or similar role, with a proven track record of optimizing machine learning ... pipelines and infrastructure. * Proficiency in cloud computing platforms (e.g., AWS, Azure, GCP ...

Senior Machine Learning Engineer

Jersey City, NJ · On-site

$127K - $168K/yr

... an MLOps Engineer or similar role, with a proven track record of optimizing machine learning ... pipelines and infrastructure. * Proficiency in cloud computing platforms (e.g., AWS, Azure, GCP ...

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

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Mlops Machine Learning Engineer information

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.

What are the key skills and qualifications needed to thrive as an MLOps machine learning engineer?

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 job categories do people searching Mlops Machine Learning Engineer jobs in New Jersey look for? The top searched job categories for Mlops Machine Learning Engineer jobs in New Jersey are:
Infographic showing various Mlops Machine Learning Engineer job openings in New Jersey as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Senior Machine Learning Engineer

SageSure

Jersey City, NJ

$127K - $168K/yr

Full-time

Re-posted 25 days ago


Job description

Overview: 

If you're looking for the stability of a profitable, growing company with the entrepreneurial spirit of a startup, we're hiring. SageSure, a leader in catastrophe-exposed property insurance, is seeking a Machine Learning Engineer.  As a Senior Machine Learning Engineer, you'll play a crucial role in optimizing orchestration processes and ensuring fast and efficient model deployment and delivery. You'll work closely with Software Engineers and Data scientists to streamline machine learning pipelines and implement best practices for managing and deploying ML models. 

What you'd be doing: 

  • Design and implement robust, scalable, and efficient data pipelines for training machine learning models. 
  • Develop prediction pipelines to ensure seamless integration of trained models into production environments. 
  • Create APIs and microservices to facilitate communication between machine learning models and other software modules. 
  • Design, build, and manage model deployment strategies to ensure reliability, scalability, and security in production environments. 
  • Implement monitoring and logging solutions to track model performance, data quality, and system health in real-time. 
  • Optimize orchestration processes to ensure efficient deployment and management of ML models. 
  • Implement cost-saving strategies to minimize infrastructure expenses while maximizing performance. 
  • Collaborate with cross-functional teams to identify bottlenecks and implement solutions to improve workflow efficiency. 

We're looking for someone who has: 

  • Bachelor's degree in Computer Science, Engineering, Mathematics, or a related technical field.  
  • 5-7 years of experience as an MLOps Engineer or similar role, with a proven track record of optimizing machine learning pipelines and infrastructure. 
  • Proficiency in cloud computing platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes). 
  • Experience with orchestration tools and frameworks such as Airflow, Kubeflow, or MLflow. 
  • Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-Learn. 
  • Experience in deploying machine learning models in production environments and managing model lifecycle. 
  • Excellent problem-solving skills and ability to work independently as well as part of a team. 
  • Strong communication skills and ability to collaborate effectively with cross-functional teams. 

Data at SageSure 

At SageSure, data isn't just a function-it's a force for innovation. Our Data organization brings together Data Science, Business Intelligence, and Data Management to power smarter decisions and deliver meaningful impact across the business. From predictive modeling to scalable data ecosystems and actionable insights, we turn complex information into clarity that drives real results. 

We're building a team of curious, collaborative problem-solvers who are passionate about using data to make a difference. Whether you're advancing machine learning capabilities, strengthening data governance, or uncovering insights that shape strategy, you'll play a critical role in defining how SageSure uses data to lead the future of insurance.Â