ZipRecruiter

Log In

2

Entry Level Mlops Engineer Jobs (NOW HIRING)

All JobsMlops Engineer JobsEntry Level Mlops Engineer Jobs

Entry Level Mlops Engineer information

See salary details

$30K

$69.4K

$118K

How much do entry level mlops engineer jobs pay per year?

As of Jul 17, 2026, the average yearly pay for entry level mlops engineer in the United States is $69,362.00, according to ZipRecruiter salary data. Most workers in this role earn between $51,500.00 and $78,500.00 per year, depending on experience, location, and employer.

What does an Entry Level MLOps Engineer do?

An Entry Level MLOps Engineer supports the deployment, monitoring, and maintenance of machine learning models in production environments. They work closely with data scientists and software engineers to automate workflows, manage data pipelines, and ensure that models run efficiently and reliably. Their responsibilities often include version control, containerization, model testing, and setting up CI/CD pipelines. This role is a great starting point for those interested in combining machine learning with DevOps practices.

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

AspectEntry Level Mlops EngineerData Engineer
Required CredentialsBachelor's in CS, Data Science, or related; familiarity with ML toolsBachelor's in CS, Data Science, or related; strong SQL and database skills
Work EnvironmentCollaborates with data scientists and ML teams on deployment pipelinesBuilds and maintains data pipelines and storage systems
Industry UsageUsed in organizations deploying ML models into productionUsed across industries for data management and analytics

Entry Level Mlops Engineers focus on deploying and maintaining machine learning models in production environments, working closely with data scientists. Data Engineers primarily develop and manage data pipelines and infrastructure. While both roles require a background in data and programming, Mlops Engineers emphasize ML deployment tools, whereas Data Engineers concentrate on data architecture. The roles often overlap but serve distinct functions in data-driven organizations.

What are some common challenges faced by entry-level MLOps engineers in their first projects?

Entry-level MLOps engineers often encounter challenges such as understanding the integration of machine learning models into production environments, managing version control for both code and data, and ensuring reproducibility of experiments. Collaborating with data scientists, software engineers, and IT teams can also be a learning curve, especially when aligning different workflows and tools. Additionally, balancing the needs for automation, scalability, and security within ML pipelines requires adaptability and a willingness to learn new technologies quickly.

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, software development, and cloud computing, often supported by a degree in computer science or a related field. Familiarity with tools like Docker, Kubernetes, Git, CI/CD pipelines, and cloud platforms such as AWS or Azure, along with basic scripting in Python or Bash, is typically required. Strong problem-solving skills, effective communication, and a collaborative mindset help you navigate cross-functional teams and adapt to evolving project needs. These skills and qualities are crucial for efficiently deploying, monitoring, and maintaining machine learning models in dynamic production environments.
More about Entry Level Mlops Engineer jobs
What cities are hiring for Entry Level Mlops Engineer jobs? Cities with the most Entry Level Mlops Engineer job openings:
What are the most commonly searched types of Mlops Engineer jobs? The most popular types of Mlops Engineer jobs are:
What states have the most Entry Level Mlops Engineer jobs? States with the most job openings for Entry Level Mlops Engineer jobs include:
What job categories do people searching Entry Level Mlops Engineer jobs look for? The top searched job categories for Entry Level Mlops Engineer jobs are:
Entry Level Mlops Engineer jobs near you
Infographic showing various Entry Level Mlops Engineer job openings in the United States as of July 2026, with employment types broken down into 1% Locum Tenens, 94% Full Time, 2% Part Time, and 3% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $69,362 per year, or $33.3 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 7 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