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Mlops Engineer Jobs in Atlanta, GA (NOW HIRING)

IT America Inc

DevOps/MLOps Engineer

Cumming, GA · On-site

$47 - $64.50/hr

... MLOps Engineer Location: Cumming, GA Duration: Long-term contract Note: Final interview will take place onsite--only local candidates will be considered * Our Fintech client is looking for an ...

TriNet

Staff MLOps Engineer

Dunwoody, GA · On-site

A Brief Overview As a Staff MLOps Engineer, you will play a pivotal role in designing, implementing, and optimizing machine learning operations within our infrastructure. You will collaborate closely ...

TriNet

Staff MLOps Engineer

Atlanta, GA · On-site

A Brief Overview As a Staff MLOps Engineer, you will play a pivotal role in designing, implementing, and optimizing machine learning operations within our infrastructure. You will collaborate closely ...

TriNet

Staff MLOps Engineer

Dunwoody, GA · On-site

A Brief Overview As a Staff MLOps Engineer, you will play a pivotal role in designing, implementing, and optimizing machine learning operations within our infrastructure. You will collaborate closely ...

Speria

Machine Learning Operations Engineer

Atlanta, GA · On-site

$120 - $180/hr

The MLOps Engineer works closely with Machine Learning Engineers and Data Engineers to ensure that models and decisioning systems are production-ready, observable, cost-efficient, and seamlessly ...

smart folks inc

Lead AI/ML Engineer

Atlanta, GA · On-site

$98K - $129K/yr

Hands-on MLOps & Engineering Practice: * Drive the practical implementation of the MLOps strategy, directly overseeing the construction and optimization of CI/CD pipelines for AI/ML systems using ...

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Mlops Engineer information

What is an MLOps engineer?

An MLOps Engineer is responsible for deploying, monitoring, and maintaining machine learning models in production. They bridge the gap between data science and operations by automating workflows, optimizing infrastructure, and ensuring model reliability. Their role includes CI/CD for ML models, data pipeline management, and performance monitoring. They also work with cloud platforms, containerization, and orchestration tools to scale ML systems efficiently.

What are some common challenges MLOps engineers face in their daily work?

Mlops Engineers often encounter challenges in integrating new machine learning models into existing production systems while ensuring minimal downtime and maintaining data integrity. Managing the scaling and orchestration of models across various cloud or on-prem environments can be complex, requiring close coordination with data scientists and DevOps teams. Staying up to date with rapidly evolving tools and best practices is also essential in this field. Addressing these challenges provides valuable opportunities to innovate and improve both technical processes and team collaboration.

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

To thrive as an Mlops Engineer, you need strong skills in software engineering, machine learning pipelines, and cloud infrastructure, often backed by a degree in computer science, engineering, or a related field. Familiarity with tools such as Docker, Kubernetes, TensorFlow, AWS/GCP/Azure, and CI/CD systems is essential, and certifications like AWS Certified Machine Learning or Kubernetes Administrator are often valued. Effective communication, problem-solving, and teamwork are crucial soft skills for collaborating across data science and IT teams. These abilities enable Mlops Engineers to efficiently deploy, manage, and scale machine learning models in dynamic production environments.

What do you need to be a MLOps engineer?

To become a MLOps engineer, you typically need a strong background in software engineering, machine learning, and cloud platforms. Proficiency in programming languages like Python, experience with containerization tools such as Docker, and knowledge of CI/CD pipelines are essential. Certifications in cloud services and familiarity with tools like Kubernetes and ML frameworks also enhance qualifications.

Who earns more, ML engineer or MLOps engineer?

MLOps engineers typically earn slightly more than ML engineers due to their focus on deploying, maintaining, and scaling machine learning systems, which requires expertise in cloud platforms, automation, and infrastructure. Salary differences can vary based on experience, location, and company size, but MLOps roles often command higher compensation because of their specialized skill set.

What are the most commonly searched types of Mlops Engineer jobs in Atlanta, GA?

The most popular types of Mlops Engineer jobs in Atlanta, GA are:

What are popular job titles related to Mlops Engineer jobs in Atlanta, GA?

For Mlops Engineer jobs in Atlanta, GA, the most frequently searched job titles are:

What job categories do people searching Mlops Engineer jobs in Atlanta, GA look for?

The top searched job categories for Mlops Engineer jobs in Atlanta, GA are:

What cities near Atlanta, GA are hiring for Mlops Engineer jobs?

Cities near Atlanta, GA with the most Mlops Engineer job openings:

Mlops Engineer jobs near you
Infographic showing various Mlops Engineer job openings in Atlanta, GA as of August 2026, with employment types broken down into 90% Full Time, 5% Part Time, and 5% Contract. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution.

DevOps/MLOps Engineer

IT America Inc

Cumming, GA • On-site

$47 - $64.50/hr

Contractor

Re-posted 17 days ago

Job description

Position: DevOps/MLOps Engineer

Location: Cumming, GA

Duration: Long-term contract

Note: Final interview will take place onsite—only local candidates will be considered

Job Description:

  • Our Fintech client is looking for an experienced DevOps / MLOps Engineer to help build and manage cloud infrastructure, deployment pipelines, and operational systems supporting AI-driven platform initiatives. This is a high-visibility role in a fast-moving environment where candidates are expected to make an immediate impact.
  • Responsibilities:
  • Develop, implement, and maintain CI/CD pipelines using tools such as GitHub Actions, Jenkins, or similar platforms
  • Provision and manage cloud infrastructure across AWS, Azure, or GCP using infrastructure-as-code tools like Terraform or CloudFormation
  • Support containerized environments and orchestration using Docker and Kubernetes
  • Monitor system performance, respond to incidents, and help define and track service reliability metrics (SLOs/SLAs)
  • Partner with engineering teams to streamline and improve deployment processes
  • Apply security best practices, including identity and access management, secrets handling, and network security policies
  • Participate in on-call support as required

Qualifications:

  • 7+ years of experience in DevOps, Site Reliability Engineering, or platform engineering roles
  • Strong hands-on experience with AWS
  • Practical experience working with Kubernetes environments (EKS, GKE, or AKS)
  • Proficiency with IaC tools such as Terraform
  • Solid understanding of CI/CD methodologies and GitOps practices
  • Experience with monitoring and observability tools such as Datadog, Grafana, or Prometheus
  • Strong scripting ability in languages like Bash, Python, or TypeScript
  • 1+ years of experience supporting AI/ML infrastructure, including GPU-based workloads or model deployment
  • Familiarity with monorepo build systems such as Nx or similar tools
  • Exposure to LLM integrations or AI platform ecosystems
  • AWS certifications (e.g., Solutions Architect or DevOps Engineer