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Entry Level Mlops Jobs (NOW HIRING)

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How much do entry level mlops jobs pay per hour?

As of Jul 17, 2026, the average hourly pay for entry level mlops in the United States is $16.94, according to ZipRecruiter salary data. Most workers in this role earn between $15.62 and $18.03 per hour, depending on experience, location, and employer.

What is an entry level MLOps engineer?

An entry level MLOps (Machine Learning Operations) engineer is a professional who helps bridge the gap between data science and IT operations by managing, deploying, and monitoring machine learning models in production environments. They typically work under the supervision of more experienced engineers and focus on automating workflows, maintaining infrastructure, and ensuring models run smoothly at scale. Entry level MLOps engineers often use tools like Docker, Kubernetes, and cloud platforms, and collaborate with data scientists to streamline the model lifecycle from development to deployment.

What are typical challenges faced by entry-level MLOps professionals, and how can they be addressed?

Entry-level MLOps professionals often face challenges such as bridging the gap between data science and IT operations, understanding deployment pipelines, and ensuring model reproducibility. It's common to work with unfamiliar tools and cloud platforms, which can be overwhelming at first. Gaining hands-on experience through projects, seeking mentorship from senior team members, and actively participating in knowledge-sharing sessions can help overcome these hurdles and accelerate your learning. Additionally, clear communication with both data scientists and engineers is key to successful collaboration in this role.

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

To thrive as an Entry Level MLOps Engineer, you need foundational knowledge in machine learning concepts, programming (typically Python), and cloud computing, often supported by a bachelor's degree in computer science or a related field. Experience with tools like Docker, Kubernetes, CI/CD pipelines, and cloud platforms such as AWS or Azure is commonly required. Strong problem-solving skills, attention to detail, and effective communication help you collaborate with data scientists and engineers. These skills ensure reliable model deployment, streamlined workflows, and successful integration of machine learning solutions into production environments.

What is the difference between Entry Level Mlops vs Data Engineer?

AspectEntry Level MlopsData Engineer
Required CredentialsBachelor's in CS, Data Science, or related field; familiarity with cloud platformsBachelor's in CS, Software Engineering, or related; knowledge of databases and ETL processes
Work EnvironmentCollaborates with data scientists and DevOps teams on deploying ML modelsBuilds and maintains data pipelines and infrastructure for analytics
Industry UsageUsed in tech, finance, healthcare for deploying ML solutionsCommon in tech, e-commerce, finance for data management

Entry Level Mlops focuses on deploying and maintaining machine learning models, often working closely with data scientists. Data Engineers build and manage data pipelines and infrastructure. While both roles require knowledge of cloud platforms and programming, Mlops emphasizes model deployment and monitoring, whereas Data Engineers focus on data architecture and processing.

More about Entry Level Mlops jobs
What cities are hiring for Entry Level Mlops jobs? Cities with the most Entry Level Mlops job openings:
What are the most commonly searched types of Mlops jobs? The most popular types of Mlops jobs are:
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What job categories do people searching Entry Level Mlops jobs look for? The top searched job categories for Entry Level Mlops jobs are:
Entry Level Mlops jobs near you
Infographic showing various Entry Level Mlops job openings in the United States as of July 2026, with employment types broken down into 1% Locum Tenens, 86% Full Time, 12% Part Time, and 1% Contract. Highlights an 96% Physical, 1% Hybrid, and 3% Remote job distribution, with an average salary of $35,227 per year, or $16.9 per hour.
Federal AI Solutions Engineer (Entry Level)

Federal AI Solutions Engineer (Entry Level)

A-TEK Inc.

Mclean, VA

$85K - $105K/yr

Other

Medical, Dental, Vision, Retirement, PTO

Re-posted 8 days ago

Job description

A-TEK is seeking an early-career Federal AI Solutions Engineer to support the design, development, and deployment of mission-focused AI and cloud solutions across AWS and Azure environments. This role is ideal for recent graduates with a strong academic foundation in Artificial Intelligence, Machine Learning, software engineering, and cloud technologies who are eager to apply their skills to real-world federal mission challenges.

The engineer will work alongside senior architects and engineers to develop AI-enabled applications, cloud-native solutions, automation workflows, and rapid prototypes that support A-TEK's DigitalShift innovation initiatives.

This is a hybrid role based in McLean, VA. No visa sponsorship is available for this role. This role requires the ability to obtain and retain a public trust level security clearance.

Key Responsibilities

AI Solution Development

  • Assist in the design, development, and deployment of AI-powered applications and prototypes using:
    • AWS Bedrock
    • Azure OpenAI
    • LangChain, LlamaIndex, Semantic Kernel, CrewAI, or similar frameworks
  • Develop and test prompt engineering strategies, retrieval-augmented generation (RAG) workflows, and AI agent capabilities.
  • Support integration of AI solutions with APIs, enterprise systems, and cloud services.

Cloud Engineering

  • Build and maintain cloud-based environments in AWS and Azure.
  • Assist with infrastructure deployment using Infrastructure-as-Code tools such as Terraform, AWS CDK, or Bicep.
  • Support containerized deployments using ECS, EKS, or AKS.
  • Participate in CI/CD implementation and cloud automation efforts.

Technical Support & Innovation

  • Support senior engineers in troubleshooting cloud, AI, and data integration challenges.
  • Assist in developing reusable templates, accelerators, and reference architectures.
  • Participate in rapid prototyping efforts that demonstrate mission value for federal customers.

Collaboration & Knowledge Sharing

  • Collaborate with Cloud, Cybersecurity, Data Intelligence, and Agile Engineering teams.
  • Contribute to technical documentation, architecture diagrams, and knowledge-sharing initiatives.
  • Stay current with emerging AI, machine learning, and cloud technologies.

Security & Compliance

  • Support implementation of solutions that align with:
    • NIST AI Risk Management Framework (AI RMF)
    • FedRAMP requirements
    • Zero Trust principles
    • Secure coding and responsible AI practices

Required Qualifications

Education (Required)

Bachelor's degree in Computer Science with a concentration, specialization, minor, or significant coursework in Artificial Intelligence, Machine Learning, Data Science, or a closely related field.

Experience

  • 0-2 years of professional software engineering, AI/ML, or cloud development experience.
  • Relevant internships, research projects, capstone projects, graduate assistantships, or co-op experience may be substituted for professional experience.
  • Demonstrated programming experience in Python through coursework, projects, internships, or employment.

Technical Skills

  • Foundational understanding of machine learning, generative AI, and large language models.
  • Experience using cloud platforms (AWS or Azure) through coursework, internships, certifications, or projects.
  • Familiarity with:
    • REST APIs
    • Git version control
    • Containerization concepts
    • Data pipelines and databases
  • Understanding of prompt engineering, RAG concepts, or AI agents through academic or personal projects.
  • Strong analytical and problem-solving skills.

Certifications

One of the following is preferred within the first year of employment:

  • AWS Certified Cloud Practitioner or AWS Solutions Architect Associate
  • Microsoft Azure Fundamentals (AZ-900) or Azure AI Fundamentals (AI-900)

Preferred Qualifications

  • Academic or project experience with:
    • LangChain
    • LlamaIndex
    • Semantic Kernel
    • CrewAI
  • Exposure to vector databases such as Pinecone, Weaviate, FAISS, or Milvus.
  • Experience building AI, machine learning, or cloud-based capstone projects.
  • Familiarity with DevOps, MLOps, or CI/CD concepts.
  • Interest in federal government missions and emerging AI technologies.

Compensation

Salary Range

$85,000 - $105,000 annually

Salary will be determined based on education, certifications, internship experience, technical proficiency, and security clearance eligibility.

Benefits

  • Medical, Dental, and Vision Insurance
  • 401(k) with Employer Match
  • Paid Time Off and Federal Holidays
  • Professional Development and Certification Reimbursement
  • Career Growth and Mentorship Opportunities

Apply