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Mlops Junior Jobs (NOW HIRING)

Mastech Digital

Mlops Engineer

Tampa, FL · On-site

W2 Only Title: Mlops Engineer Duration: 6 Months + CTH Location: Remote (1 Week Mandatory ... Provide technical leadership and mentorship to junior data team members. Additional Duties and ...

ManTech

Junior AI/ML Engineer

Arlington, VA · Remote

$83K - $139K/yr

Familiarity with MLOps frameworks such as AWS SageMake, MLflow, Kubeflow, or Airflow. * Expertise in scalable model serving platforms such as TensorFlow Serving, TorchServe, or ONNX runtime.

Huntington

MLOps Automation Senior Lead Engineer

Chicago, IL · On-site +1

$107K - $140K/yr

The MLOps Automation Engineering Senior Lead will lead a team responsible for building and ... Ability to train more junior analysts regarding day-to-day activities, as necessary * Proven ...

Huntington

MLOps Automation Senior Lead Engineer

Austin, TX · On-site +1

$103K - $135K/yr

The MLOps Automation Engineering Senior Lead will lead a team responsible for building and ... junior analysts regarding day-to-day activities, as necessary Proven ability to lead cross ...

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How much do mlops junior jobs pay per hour?

As of Jul 16, 2026, the average hourly pay for mlops junior in the United States is $26.96, according to ZipRecruiter salary data. Most workers in this role earn between $16.35 and $33.17 per hour, depending on experience, location, and employer.

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

To thrive as an MLOps Junior, you need a solid understanding of machine learning principles, programming in Python, and knowledge of software development practices, often supported by a degree in computer science or a related field. Familiarity with cloud platforms (such as AWS or Azure), containerization tools (like Docker), CI/CD pipelines, and version control systems (like Git) is typically required. Strong problem-solving, collaboration, and a willingness to learn stand out as soft skills in this role. These skills and qualities are crucial for efficiently deploying, maintaining, and scaling machine learning models in real-world production environments.

What are MLOps Junior roles?

MLOps Junior roles focus on supporting the deployment, maintenance, and monitoring of machine learning models in production environments. As a junior professional, you typically assist in automating workflows, managing data pipelines, and collaborating with data scientists and engineers. Responsibilities often include configuring cloud resources, setting up CI/CD pipelines, and ensuring models run smoothly after deployment. It's an entry-level position that helps bridge the gap between data science and operations, providing hands-on experience with machine learning infrastructure.

What are some common challenges faced by a Junior MLOps professional in their first year, and how can they overcome them?

As a Junior MLOps professional, one of the main challenges is bridging the gap between data science and engineering practices, particularly when deploying machine learning models to production. You may encounter issues like automating model pipelines, managing dependencies, and ensuring reproducibility. Collaborating closely with data scientists, software engineers, and DevOps teams is essential for learning best practices and troubleshooting problems efficiently. Proactively seeking mentorship, participating in code reviews, and familiarizing yourself with popular MLOps tools (such as Docker, Kubernetes, and CI/CD platforms) can greatly accelerate your growth and confidence in the role.

What is the difference between Mlops Junior vs Data Engineer?

AspectMlops JuniorData Engineer
Required CredentialsBasic understanding of ML workflows, some certifications preferredDegree in Computer Science or related field, certifications in data management
Work EnvironmentCollaborates with data scientists and ML engineers in tech companiesWorks on data pipelines, storage, and processing systems in various industries
Industry UsageEmerging role in AI/ML teams, startups, and tech firmsEstablished role across finance, healthcare, tech, and more

The comparison shows that Mlops Junior and Data Engineer roles share some technical foundations but differ mainly in focus. Mlops Junior emphasizes deploying and maintaining ML models, while Data Engineers focus on building data infrastructure. Both roles are vital in data-driven organizations, with Mlops Junior often working closely with Data Engineers to ensure smooth ML operations.

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Mlops Junior jobs near you
Mlops Engineer

Mlops Engineer

Mastech Digital

Tampa, FL • On-site

Other

Posted 4 days ago

Job description

W2 Only


Title: Mlops Engineer

Duration: 6 Months + CTH

Location: Remote (1 Week Mandatory onboarding in tampa , FL expenses will be paid)

(ONLY W2)


Job Description:

Essential Duties and Responsibilities:

  • Design, implement, and maintain machine learning pipelines and workflows for the continuous deployment and integration of machine learning models. Optimize the pipelines for scalability, efficiency, and cost-effectiveness.
  • Collaborate with data scientists and AI engineers to understand model requirements and optimize deployment processes.
  • Automate the training, testing, and deployment processes for machine learning models.
  • Establish and enforce best practices for version control, documentation, and code quality in ML projects.
  • Monitor model performance and optimize algorithms for efficiency.
  • Conduct regular maintenance and updates to deployed models.
  • Collaborate with cross-functional teams to integrate machine learning solutions into business processes and applications.
  • Work with go to market, product management, and IT functions as well as stakeholders in AF and its members to identify the optimal methods for model rollout and adoption.
  • Maintain and optimize the cloud-based machine learning infrastructure and make recommendations for improvements.
  • Manage and allocate resources effectively, including computer power and storage for model inference.
  • Develop practices and utilize tools for data validation, model testing, and versioning.
  • Troubleshoot and resolve machine learning operational issues.
  • Document processes, workflows, and best practices for ML Operations.
  • Provide technical leadership and mentorship to junior data team members.


Additional Duties and Responsibilities

  • Support data observability efforts to ensure the data continuum and enforce governance standards.
  • Other duties assigned by manager or project needs.


Qualifications

  • Bachelor's or Master's degree in Computer Science, Information Systems, Data Science, or a related field.
  • Minimum of 6 years of experience in data science, machine learning, data management, data governance, or related role.
  • Minimum of 6 years as a MLOps Engineer or in a similar role.


Technical Skills:

  • Working knowledge of cloud services (i.e., MS Azure, AWS, Google Cloud).
  • Experience with AI tools, such as MS Azure ML, Snowflake, Databricks, CortexAI, Dataiku.
  • Deep understanding of data science principles, algorithms, and tools.
  • Strong knowledge of data governance, data security, and compliance practices.
  • Proficiency in programming languages such as Python, R, or Java.
  • Experience with containerization tools like Docker and orchestration tools like Kubernetes.
  • Proficiency in ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
  • Working knowledge of CI/CD pipelines, DevOps practices, and automation frameworks.
  • Deep understanding of data engineering concepts and tools.
  • Familiarity with data visualization and reporting tools (e.g., Webfocus, Power BI).

Soft Skills:

  • Excellent analytical and problem-solving abilities.
  • Strong communication and interpersonal skills to collaborate with cross-functional teams.
  • Ability to lead projects and mentor junior staff.
  • Auto Insurance claims industry experience preferred.


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