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Senior Machine Learning Ops Engineer Jobs in Georgia

CNN

Machine Learning Engineer I

Atlanta, GA · On-site

CNN is a global leader in news and information, seeking a Machine Learning Engineer I to build and deploy ML systems that enhance personalization, search, recommendations, and content understanding ...

Waystar

Senior ML Engineer

Atlanta, GA · On-site

$100K - $138K/yr

They are seeking a Senior Machine Learning Engineer to develop robust AI systems utilizing Language Models and agentic architectures, focusing on the entire ML pipeline from data extraction to ...

Cox Automotive

Machine Learning Lead Engineer

Marietta, GA · On-site

$134K - $224K/yr

We are seeking a visionary Machine Learning Engineer Lead to spearhead our experimental ML initiatives and drive innovation across the organization. This role combines technical leadership in cutting ...

Cox Automotive

Machine Learning Lead Engineer

Redan, GA · On-site

$134K - $224K/yr

We are seeking a visionary Machine Learning Engineer Lead to spearhead our experimental ML initiatives and drive innovation across the organization. This role combines technical leadership in cutting ...

Cox Automotive

Machine Learning Lead Engineer

Doraville, GA · On-site

$134K - $224K/yr

We are seeking a visionary Machine Learning Engineer Lead to spearhead our experimental ML initiatives and drive innovation across the organization. This role combines technical leadership in cutting ...

Cox Automotive

Machine Learning Lead Engineer

Hapeville, GA · On-site

$134K - $224K/yr

We are seeking a visionary Machine Learning Engineer Lead to spearhead our experimental ML initiatives and drive innovation across the organization. This role combines technical leadership in cutting ...

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Senior Machine Learning Ops Engineer information

What are the key skills and qualifications needed to thrive as a Senior Machine Learning Ops Engineer, and why are they important?

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 Senior Machine Learning Ops Engineers?

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.
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Senior Machine Learning Ops Engineer jobs near you
Machine Learning Engineer I

Machine Learning Engineer I

CNN

Atlanta, GA • On-site

Full-time

Posted 24 days ago

Job description

Job Summary:
CNN is a global leader in news and information, seeking a Machine Learning Engineer I to build and deploy ML systems that enhance personalization, search, recommendations, and content understanding for millions of users. The role involves collaborating with cross-functional teams and working on production ML systems that have measurable product impact.
Responsibilities:
• Build and deploy full-lifecycle machine learning systems in Python for CNN digital products, including personalization, search, recommendations, and content understanding
• Develop and maintain production ML pipelines, including feature engineering, model training, evaluation, and serving infrastructure
• Implement rigorous experimentation and A/B testing frameworks to validate model performance and product impact
• Optimize ML systems for real-time, web-scale performance serving millions of users
• Partner with platform and infrastructure teams to ensure ML systems meet reliability, scalability, and performance standards
• Contribute to code reviews, documentation, and team knowledge sharing
Qualifications:
Required:
• Graduate degree (MS or PhD) in Computer Science, Mathematics, Statistics, Engineering, or a related quantitative field
• 1+ years of professional experience building and deploying machine learning systems in production environments
• Strong Python programming skills and experience with machine learning frameworks (e.g., scikit-learn or similar)
• Experience across the full ML lifecycle, including data preprocessing, feature engineering, model training, evaluation, and deployment
• Solid understanding of software engineering best practices, including version control, testing, and CI/CD
• Ability to collaborate effectively with cross-functional partners
• Strong communication skills, with the ability to explain technical concepts to non-technical stakeholders
Preferred:
• Experience working on large-scale consumer internet products (e.g., social, streaming, e-commerce, media)
• Hands-on experience with recommendation systems, search, NLP, or information retrieval
• Familiarity with data pipelines, feature stores, or embedding infrastructure
• Experience with experimentation platforms, A/B testing, and experimentation analysis
• Knowledge of cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes)
• Interest in generative AI applications and/or the media and news industry
Company:
CNN Worldwide is the most honored brand in cable news, reaching more individuals on television and online than any other cable news organization in the United States. Founded in 1980, the company is headquartered in Jakarta, IDN, with a team of 1001-5000 employees. The company is currently Late Stage.

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