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Mlops Machine Learning Engineer Jobs in New Jersey

Chefman

MLOps Engineer / DevOps Engineer

Mahwah, NJ · On-site

$53 - $72.50/hr

MLOps Engineer / DevOps Engineer *Candidates must be legally authorized to work in the United ... From machine learning and computer vision to Generative AI applications, our success depends on ...

SageSure

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 ...

SageSure

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 ...

Bespoke Labs

Machine Learning Engineer

Edison, NJ · On-site

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 ...

Bespoke Labs

Machine Learning Engineer

Paterson, NJ · On-site

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 ...

Bespoke Labs

Machine Learning Engineer

Newark, NJ · On-site

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 ...

Bespoke Labs

Machine Learning Engineer

Elizabeth, NJ · On-site

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 ...

Bespoke Labs

Machine Learning Engineer

Trenton, NJ · On-site

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 are popular job titles related to Mlops Machine Learning Engineer jobs in New Jersey? For Mlops Machine Learning Engineer jobs in New Jersey, the most frequently searched job titles are:
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:
Mlops Machine Learning Engineer jobs near you
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, 75% Full Time, 21% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

MLOps Engineer / DevOps Engineer

Chefman

Mahwah, NJ • On-site

$53 - $72.50/hr

Full-time

Re-posted 16 days ago

Job description

MLOps Engineer / DevOps Engineer

*Candidates must be legally authorized to work in the United States on a permanent and ongoing basis without the need for current or future employer-sponsored visa support, including H-1B, OPT, STEM OPT, or any other work authorization requiring sponsorship. Applications from candidates requiring sponsorship now or in the future will not be considered.

In 2020, we launched CHEF iQ, an ecosystem of connected kitchen appliances designed to transform how people cook and connect through food. Our mission is to make great cooking effortless through intelligent technology, guided experiences, and seamless integration between hardware, software, and AI. As CHEF iQ continues to expand its AI capabilities, we are building the infrastructure and platforms that will power the next generation of connected cooking experiences. From machine learning and computer vision to Generative AI applications, our success depends on scalable, reliable systems that enable rapid innovation and deployment.
 
We are seeking a highly detail-oriented MLOps / DevOps Engineer to serve as a critical partner to our Machine Learning Engineer, building and maintaining the cloud infrastructure, deployment pipelines, automation frameworks, and operational foundations that support AI development at scale. This individual will play a key role in improving engineering efficiency, increasing system reliability, and ensuring our AI-powered products can be developed, deployed, and scaled successfully.
 
The ideal candidate is passionate about automation, process improvement, and building highly scalable systems. They enjoy creating order from complexity, eliminating operational bottlenecks, and enabling teams to move faster. Experience supporting AI, machine learning, and Generative AI applications in production environments is required.
 
Role and Responsibilities
  • Design, implement, and maintain scalable AWS cloud infrastructure supporting software, AI, and machine learning applications.
  • Create and manage MLOps infrastructure for model training, deployment, monitoring, versioning, and lifecycle management.
  • Partner closely with the Machine Learning Engineer to establish the tools, workflows, and infrastructure required for successful AI development and deployment.
  • Support Generative AI initiatives by building infrastructure and deployment frameworks for applications utilizing AWS Bedrock, foundation models, LLMs, and related AI services.
  • Build and manage Infrastructure as Code (IaC) using Terraform to ensure repeatable, secure, and scalable environments.
  • Implement monitoring, logging, observability, and alerting systems across software, infrastructure, and machine learning platforms.
  • Continuously identify opportunities to improve engineering processes, reduce manual effort, increase automation, and improve system reliability.
  • Develop and maintain CI/CD pipelines that enable rapid, reliable software and machine learning deployments.
  • Optimize cloud environments for scalability, performance, availability, and cost efficiency.
  • Support security, compliance, backup, disaster recovery, and operational best practices across all environments.
  • Troubleshoot infrastructure, deployment, and application issues across development, testing, and production environments.
  • Document infrastructure architecture, deployment processes, operational procedures, and engineering standards.
  • Contribute to establishing best practices for DevOps, MLOps, cloud architecture, and AI operations.
 
Qualifications

Please Note: Chefman is unable to provide visa sponsorship for this position. Candidates must be legally authorized to work in the United States on a permanent and ongoing basis without the need for current or future employer-sponsored visa support, including H-1B, OPT, STEM OPT, or any other work authorization requiring sponsorship. Applications from candidates requiring sponsorship now or in the future will not be considered.

  • 5+ years of experience in MLOps, DevOps, Site Reliability Engineering (SRE), Platform Engineering, or related software engineering roles.
  • Strong hands-on experience with AWS services and cloud-native architecture.
  • Experience building and supporting AI and machine learning platforms in AWS environments.
  • Experience supporting AI, machine learning, and Generative AI applications in production environments.
  • Experience working with AWS Bedrock, Generative AI services, foundation models, LLM-powered applications, or related AI infrastructure.
  • Strong experience implementing Infrastructure as Code using Terraform.
  • Experience with containerization and orchestration technologies such as Docker and Kubernetes.
  • Strong experience building and maintaining CI/CD pipelines and deployment automation.
  • Experience supporting machine learning workflows, model deployment, monitoring, and MLOps platforms.
  • Strong programming and scripting skills in Python, Bash, or similar languages.
  • Experience with monitoring, logging, observability, and operational tooling.
  • Strong troubleshooting, systems-thinking, and problem-solving abilities.
  • Excellent communication and cross-functional collaboration skills.
  • Proven track record of improving engineering processes, increasing operational efficiency, and scaling software platforms.
  • Highly organized and detail-oriented with a passion for automation and continuous improvement.
 
Preferred Qualifications
  • Experience with vector databases, retrieval-augmented generation (RAG), model serving, and AI infrastructure.
  • Experience supporting connected devices, IoT platforms, embedded systems, or consumer technology products.
  • Domain expertise in machine learning infrastructure, AI platforms, consumer applications, connected products, or similar technology environments.
  • Experience working in fast-paced startup or high-growth product organizations.

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