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Mlops Junior Jobs (NOW HIRING)

Striim, Inc.

OR · On-site

$120K - $130K/yr

We are seeking a Junior Solution Architect with a strong foundation in data science, MLOps, cloud data platforms, and modern data engineering to help design and implement real-time data integration ...

New

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 ...

SCCI

Junior Engineer/Analyst

King George, VA · On-site

Overview SCCI is currently seeking a Junior Engineer/Analyst with a focus on DevOps/MLOps to join our team! In this position you will support of the Battle Management System (BMS) Tactical ...

Quince

Staff Engineer - ML Infra / MLOps

Palo Alto, CA · On-site

THE ROLE Staff Engineer for ML Infra / MLOps We are seeking a Staff ML Engineer to join our growing ... Provide deep technical mentorship to junior and mid-level engineers through design reviews, code ...

New

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 ...

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.

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How much do mlops junior jobs pay per hour?

As of Jul 26, 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
Infographic showing various Mlops Junior job openings in the United States as of July 2026, with employment types broken down into 96% Full Time, 3% Part Time, and 1% Contract. Highlights an 92% Physical, 3% Hybrid, and 5% Remote job distribution, with an average salary of $56,068 per year, or $27 per hour.
Junior Solutions Architect - MLOps & Real-Time Data Integration

Junior Solutions Architect - MLOps & Real-Time Data Integration

Striim, Inc.

OR • On-site

$120K - $130K/yr

Other

Medical, Dental, Vision, PTO

Posted yesterday

Job description

We are seeking a Junior Solution Architect with a strong foundation in data science, MLOps, cloud data platforms, and modern data engineering to help design and implement real-time data integration and AI-enabled architectures. Working alongside experienced Solution Architects and Engineering teams, this role provides an opportunity for an early-career professional to gain hands-on experience designing scalable streaming data solutions that power enterprise AI, cloud modernization, and real-time analytics.

The ideal candidate is eager to apply data science and machine learning concepts to real-world enterprise challenges, expand technical expertise across modern cloud and data technologies, and develop into a trusted technical architect within a collaborative, fast-paced environment.

Responsibilities

  • Design and implement scalable real-time data integration and Change Data Capture (CDC) solutions using the Striim platform.
  • Design streaming data architectures connecting enterprise databases, cloud data platforms, messaging systems, and AI/ML environments.
  • Develop data pipelines that support machine learning workflows, feature engineering, model inference, and real-time AI applications.
  • Build proof-of-concepts, reference architectures, and deployment patterns for enterprise implementations.
  • Configure, optimize, and troubleshoot data pipelines across cloud and hybrid environments.
  • Collaborate with Engineering, Product, and GTM Engineering teams to validate architectural designs, resolve complex technical challenges, and improve platform capabilities.
  • Participate in architecture reviews, implementation planning, and production readiness activities.
  • Create technical documentation, architecture diagrams, and implementation best practices.
  • Evaluate emerging technologies across AI, MLOps, cloud computing, and real-time data streaming.

Requirements

  • 1-3 years of professional experience or equivalent graduate research, internships, or project experience in data science, machine learning, data engineering, cloud engineering, or solution architecture.
  • Strong foundation in data science, including machine learning algorithms, model selection, feature engineering, and the machine learning lifecycle.
  • Understanding of modern MLOps practices, including model deployment, inference, monitoring, versioning, and CI/CD for machine learning applications.
  • Experience or academic exposure to machine learning frameworks and platforms such as MLflow, Kubeflow, Vertex AI, SageMaker, or Azure Machine Learning.
  • Familiarity with modern data integration concepts, including Change Data Capture (CDC), event-driven architectures, and real-time streaming data pipelines.
  • Working knowledge of relational and NoSQL databases, including SQL proficiency and database administration fundamentals.
  • Experience with cloud platforms and modern cloud data ecosystems, including AWS, Azure, GCP, Databricks, Snowflake, BigQuery, Amazon Redshift, or Azure Synapse.
  • Experience programming in Python or Java and working with REST APIs and JSON.
  • Understanding of Docker containers and modern DevOps concepts; familiarity with Kubernetes, Git, and CI/CD pipelines.
  • Strong analytical, troubleshooting, written, and verbal communication skills.
  • Demonstrated curiosity, adaptability, and a passion for learning emerging technologies in AI, cloud computing, and real-time data streaming.
  • Bachelor's or Master's degree in Computer Science, Data Science, Software Engineering, Information Systems, or a related technical discipline.

Benefits

  • Competitive salary and pre-IPO stock options
  • Comprehensive health care plans (medical, dental and vision), including medical and dependent FSA
  • Paid Time Off (Vacation, Sick & Public Holidays)
  • The chance to contribute to and shape an upbeat, fully engaged culture

Compensation

$120,000 - $130,000 USD on an annualized basis. In addition to base pay, this role offers the opportunity to earn commission-based rewards.

Applications will be reviewed on a rolling basis and accepted until the position is filled.

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