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Mlops Machine Learning Engineer Jobs in New Jersey

Lead, Machine Learning Engineer

Newark, NJ · On-site

$107K - $141K/yr

As a Lead, Machine Learning Engineer, you will partner with Data Scientists, Data Engineers, Data Analysts and other professionals to implement machine learning models that will deliver stability ...

$111K - $145K/yr

Job Requisition ID # 26WD98377 Senior Machine Learning Test Engineer Location: United States East ... Your skills span test strategy, automation, and a little MLOps, with a strong software engineering ...

Machine Learning Engineer

Livingston, NJ · On-site

$62K - $100K/yr

As an AI Engineering team member, you will be instrumental in advancing new features and/or solutions from the Proof of Concept stage to full production readiness. Your role involves refining and ...

We are looking for an AI / Machine Learning Engineer to design, build, and deploy advanced computer vision and AI solutions. You will work on projects involving image capture , data extraction , and ...

We are looking for an AI / Machine Learning Engineer to design, build, and deploy advanced computer vision and AI solutions. You will work on projects involving image capture , data extraction , and ...

We are looking for an AI / Machine Learning Engineer to design, build, and deploy advanced computer vision and AI solutions. You will work on projects involving image capture , data extraction , and ...

We are looking for an AI / Machine Learning Engineer to design, build, and deploy advanced computer vision and AI solutions. You will work on projects involving image capture , data extraction , and ...

Showing results 21-40

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.

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.

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 machine learning engineers in demand?

MLOps machine learning engineers are in high demand due to the increasing adoption of AI and machine learning across industries. They are needed to develop, deploy, and maintain scalable ML systems, often requiring skills in cloud platforms, automation, and tools like Docker and Kubernetes. The role offers strong job growth prospects and competitive salaries.

Do MLOps Machine Learning Engineers need a degree?

MLOps Machine Learning Engineers typically do not require a formal degree but often have a background in computer science, data science, or related fields. Practical skills in machine learning, cloud platforms, and tools like Docker, Kubernetes, and CI/CD pipelines are highly valued. Certifications and hands-on experience can also enhance job prospects.
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, 78% Full Time, 20% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Lead, Machine Learning Engineer

Prudential

Newark, NJ • On-site

$107K - $141K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 20 days ago


Prudential rating

8.7

Company rating: 8.7 out of 10

Based on 49 frontline employees who took The Breakroom Quiz

73rd of 315 rated insurance


Job description

Job Classification:

Technology - Data Analytics & Management

Are you interested in building capabilities that enable the organization with innovation, speed, agility, scalability and efficiency? The Global Technology team takes great pride in our culture where digital transformation is built into our DNA! When you join our organization at Prudential, you'll unlock an exciting and impactful career - all while growing your skills and advancing your profession at one of the world's leading financial services institutions.

As a Lead, Machine Learning Engineer, you will partner with Data Scientists, Data Engineers, Data Analysts and other professionals to implement machine learning models that will deliver stability, producibility, scalability and integration with other products and services. You will implement capabilities to solve sophisticated business problems, deploy innovative products, services and experiences to delight our customers! In addition to advanced technical expertise and experience, you will bring excellent problem solving, communication and teamwork skills, along with agile ways of working, strong business insight, an inclusive leadership attitude and a continuous learning focus to all that you do.

Here is what you can expect in a typical day:

  • Operationalize ML software models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams; remove complex technical impediments
  • Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
  • Leverage cloud-based architectures and technologies to deliver optimized ML models at scale
  • Construct optimized data pipelines to feed ML models
  • Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code
  • Bring a strong understanding of relevant and emerging technologies, provide input and coach team members and embed learning and innovation in the day-to-day
  • Work on complex problems in which analysis of situations or data requires an evaluation of intangible variables.
  • Use programming languages including but not limited to Python, R, SQL, Java or Scala, SQL

The Skills and expertise you bring:

  • Bachelor of Computer Science or Engineering or experience in related fields
  • Ability to coach others with minimal guidance and effectively leverage diverse ideas, experiences, thoughts and perspectives to the benefit of the organization
  • Experience with agile development methodologies and Test-Driven Development (TDD)
  • Knowledge of business concepts, tools and processes that are needed for making sound decisions in the context of the company's business
  • Ability to learn new skills and knowledge on an on-going basis through self-initiative and tackling challenges
  • Excellent problem solving, communication and collaboration skills

Advanced experience and/or expertise with several of the following:

  • Software Engineering & System Design: Requirement analysis, coding, and testing, version control, microservices architecture, building RestFul APIs, Distributed computing, architecture patterns, general understanding of computer architecture, Object-oriented programming concepts
  • Machine Learning and Deep Learning: Good understanding of: ML algorithms like linear regression, logistic regression, etc., supervised, unsupervised, and reinforcement learning, AI Frameworks like TensorFlow, PyTorch, scikit-learn etc., Neural network, NLP, computer vision, and predictive analytics
  • Model Performance Management: model monitoring, model validation, bias detection, explainability, performance, drift, outliers etc.
  • Model Deployment: Thorough Understanding of MDLC (Model Development Life Cycle), CI/CD/CT pipelines (using tools like Jenkins, CloudBees, Harness etc.), A/B testing. Pipeline frameworks like MLFlow, AWS SageMaker pipeline etc. model and data versioning
  • Data Integration, Transformation & Processing: Transforming and mapping raw data to generate insights. Data wrangling through various tools. Understanding big data ecosystems, relational, NOSQL and graph databases, unstructured and semi-structured data. Data processing on distributed systems with Spark/PySpark
  • Statistics and Computing: Strong knowledge of: Linear Algebra, Probability and Statistics, Multivariate Calculus, Distributions like Poisson, Normal, Binomial etc.
  • Programming Languages: Python, R, SQL, Java or Scala, SQL
You'll Love Working Here Because You Can

Join a team and culture where your voice matters; where every day, your work transforms our experiences to make lives better. As you put your skills to use, we'll help you make an even bigger impact with learning experiences that can grow your technical AND leadership capabilities. You'll be surprised by what this rock-solid organization has in store for you.

What we offer you:Prudential is required by state specific laws to include the salary range for this role when hiring a resident in applicable locations. The salary range for this role is from $125,000.00 to $229,700.00. Specific pricing for the role may vary within the above range based on many factors including geographic location, candidate experience, and skills.
  • Market competitive base salaries, with a yearly bonus potential at every level.

  • Medical, dental, vision, life insurance, disability insurance, Paid Time Off (PTO), and leave of absences, such as parental and military leave.

  • 401(k) plan with company match (up to 4%).

  • Company-funded pension plan.

  • Wellness Programs including up to $1,600 a year for reimbursement of items purchased to support personal wellbeing needs.

  • Work/Life Resources to help support topics such as parenting, housing, senior care, finances, pets, legal matters, education, emotional and mental health, and career development.

  • Education Benefit to help finance traditional college enrollment toward obtaining an approved degree and many accredited certificate programs.

  • Employee Stock Purchase Plan: Shares can be purchased at 85% of the lower of two prices (Beginning or End of the purchase period), after one year of service.

Eligibility to participate in a discretionary annual incentive program is subject to the rules governing the program, whereby an award, if any, depends on various factors including, without limitation, individual and organizational performance. To find out more about our Total Rewards package, visit Work Life Balance | Prudential Careers. Some of the above benefits may not apply to part-time employees scheduled to work less than 20 hours per week.

Prudential Financial, Inc. of the United States is not affiliated with Prudential plc. which is headquartered in the United Kingdom.

Prudential is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, ancestry, sex, sexual orientation, gender identity, national origin, genetics, disability, marital status, age, veteran status, domestic partner status, medical condition or any other characteristic protected by law.

If you need an accommodation to complete the application process, please email accommodations.hw@prudential.com.

If you are experiencing a technical issue with your application or an assessment, please email careers.technicalsupport@prudential.com to request assistance.


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