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Senior Machine Learning Ops Engineer Jobs in Georgia

W2 Candidates Only We are seeking a Machine Learning Engineer to develop, deploy, and optimize machine learning models and AI solutions. The ideal candidate will have strong experience with Python ...

Equifax is excited to add a Machine Learning Engineer to our team. What you'll do * Design complex systems of systems for training and running machine learning models with industry best practice

Senior/Principal AI Engineer

Atlanta, GA

$120K - $166K/yr

About the Role As a Senior/Principal Machine Learning Engineer in Agent Factory, you'll design and build the core ML systems behind Workday's next generation of AI agents. Working within a small ...

Senior/Principal AI Engineer

Atlanta, GA · On-site

$120K - $166K/yr

About the Role As a Senior/Principal Machine Learning Engineer in Agent Factory, you'll design and build the core ML systems behind Workday's next generation of AI agents. Working within a small ...

... work with senior and executive management. Job Summary We are seeking a highly skilled and ... The MLOps Engineer works closely with Machine Learning Engineers and Data Engineers to ensure that ...

Showing results 21-40

Senior Machine Learning Ops Engineer information

What is a senior machine learning ops engineer?

Senior Machine Learning Ops (MLOps) Engineers are experienced professionals who design, build, and maintain the infrastructure and tools needed to deploy, monitor, and scale machine learning models in production environments. They work at the intersection of data science, software engineering, and DevOps to ensure ML models are robust, reliable, and secure. Their responsibilities often include automating model training pipelines, managing cloud resources, implementing CI/CD for ML, and ensuring model reproducibility. Senior MLOps Engineers also mentor junior staff and help define best practices for the organization’s ML workflow.

What are the key skills and qualifications needed to thrive as a senior machine learning ops engineer?

To thrive as a Senior Machine Learning Ops Engineer, you need expertise in machine learning, software engineering, cloud platforms, and experience with CI/CD pipelines, often supported by a computer science degree or equivalent experience. Proficiency with tools like Docker, Kubernetes, TensorFlow, PyTorch, and cloud services such as AWS, GCP, or Azure is typically required, along with familiarity with MLOps frameworks. Strong problem-solving, collaboration, and communication skills help you work effectively with cross-functional teams and manage complex ML model deployments. These skills are essential to ensure reliable, scalable, and efficient deployment of machine learning models in production environments.

What are some common challenges faced by senior machine learning ops engineers when deploying models to production?

Senior Machine Learning Ops Engineers often encounter challenges such as ensuring model reproducibility, managing model versioning, and automating deployment pipelines for scalability. Another key challenge is monitoring model performance and data drift in production, which requires robust logging and alerting systems. Collaborating closely with data scientists, software engineers, and IT teams is essential to address these challenges and maintain a stable, efficient ML infrastructure.

What is the difference between Senior Machine Learning Ops Engineer vs Data Engineer?

AspectSenior Machine Learning Ops EngineerData Engineer
CredentialsExperience with ML frameworks, cloud platforms, scripting, and DevOps toolsStrong SQL, ETL, database, and programming skills, often with cloud experience
Work EnvironmentFocus on deploying, monitoring, and maintaining ML models in productionDesigning and building data pipelines and infrastructure for data processing
Industry UsageCommon in AI/ML-focused companies, tech firms, and data-driven organizationsWidespread across industries for data management and analytics

While both roles involve working with data and cloud platforms, the Senior Machine Learning Ops Engineer specializes in deploying and maintaining machine learning models, whereas the Data Engineer focuses on building data pipelines and infrastructure. Understanding these distinctions helps in choosing the right career path or job search focus.

What are the most commonly searched types of Machine Learning Ops Engineer jobs in Georgia?

The most popular types of Machine Learning Ops Engineer jobs in Georgia are:

What cities in Georgia are hiring for Senior Machine Learning Ops Engineer jobs?

Cities in Georgia with the most Senior Machine Learning Ops Engineer job openings:

Infographic showing various Senior Machine Learning Ops Engineer job openings in Georgia as of June 2026, with employment types broken down into 1% Internship, 82% Full Time, 13% Part Time, 1% Temporary, and 3% Contract. Highlights an 89% Physical, 3% Hybrid, and 8% Remote job distribution.

Machine Learning Engineer

Atlanta, GA • On-site

innovitusa
IT Services • 11 - 50 employees

Contractor

This job post has expired today. Applications are no longer accepted.


Job description

Hiring: W2 Candidates Only

Job Description:
We are seeking a Machine Learning Engineer to develop, deploy, and optimize machine learning models and AI solutions. The ideal candidate will have strong experience with Python, machine learning frameworks, data processing, and cloud-based ML platforms.

Required Skills:

  • 5+ years of Machine Learning experience
  • Python
  • Scikit-learn
  • TensorFlow or PyTorch
  • Machine Learning algorithms
  • Pandas and NumPy
  • SQL
  • Model development and evaluation
  • Data preprocessing
  • Model deployment

Preferred Skills:

  • Generative AI
  • Large Language Models (LLMs)
  • NLP
  • Hugging Face
  • AWS SageMaker or Azure ML
  • MLflow
  • Docker and Kubernetes
  • MLOps
  • Experience with AI/ML production environments

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