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Mlops Machine Learning Engineer Jobs in Georgia (NOW HIRING)

Five and Fly

Machine Learning Engineer

Atlanta, GA · On-site

Machine Learning Engineer Employment Type: Full Time Location: Atlanta, GA Description We are ... Experience with CI/CD for ML (MLOps), monitoring, and observability. * Familiarity with anomaly ...

Equifax, Inc.

Machine Learning Engineer

Atlanta, GA · On-site

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

PrizePicks

Staff Machine Learning Engineer

Atlanta, GA · On-site +1

$220K - $280K/yr

As a Staff Machine Learning Engineer, you will lead the technical charge to scale and productionize ... End-to-End MLOps Leadership: Champion best practices for model deployment, monitoring, and CI/CD ...

Speria

Machine Learning Engineer

Atlanta, GA · On-site

$120 - $160/hr

Job Summary We are seeking a highly skilled and motivated Machine Learning Engineer to join our dynamic team at Speria MTech. The ideal candidate will play a crucial role in designing, building, and ...

KSB GIW, Inc.

Machine Learning Engineer

Grovetown, GA · On-site

$70 - $110/hr

Machine Learning Engineer Department: Engineering, Research & Development Reports to: Metallurgical and Materials R&D Lab Manager Location: Grovetown, GA, USA (onsite) Shift: First FLSA Status:

KSB, Inc.

Machine Learning Engineer

Grovetown, GA · On-site

$70 - $95/hr

Machine Learning Engineer**### **KSB GIW, Inc.**### **Department:** Engineering, Research & Development **Reports to:** Metallurgical and Materials R&D Lab Manager **Location:** Grovetown, GA, USA ...

Ksb

Machine Learning Engineer

Grovetown, GA

Machine Learning Engineer KSB GIW, Inc. Department: Engineering, Research & Development Reports to: Metallurgical and Materials R&D Lab Manager Location: Grovetown, GA, USA (onsite) Shift: First FLSA ...

Coca-Cola

Senior Machine Learning Engineer (MLOPS)

Atlanta, GA · On-site

$100K - $138K/yr

Collaborate with data engineering teams to ensure clean, reliable data pipelines (such as Medallion architectures) seamlessly feed into machine learning models. * Engineering Best Practices: Write ...

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 ...

Warnerbros

Senior Machine Learning Engineer

Atlanta, GA · On-site

$100K - $138K/yr

Senior Machine Learning Engineer Team: Data & Audience Platform (DAP) - ML Engineering What We Do ... MLOps & Infrastructure Champion MLOps best practices: model versioning, champion/challenger ...

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Mlops Machine Learning Engineer information

What does an MLOps machine learning engineer do?

An MLOps Machine Learning Engineer bridges the gap between data science and IT operations by developing, deploying, and maintaining machine learning models in production environments. They are responsible for automating workflows, managing model versioning, monitoring performance, and ensuring scalability and reliability of ML systems. Their work enables organizations to deploy machine learning solutions efficiently and consistently, making it easier to update and manage models as business needs evolve.

What are the key skills and qualifications needed to thrive as an MLOps machine learning engineer?

To thrive as an MLOps Machine Learning Engineer, you need a strong background in machine learning concepts, software engineering, and cloud infrastructure, typically supported by a degree in computer science or a related field. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (AWS, GCP, Azure), and certifications such as Google Professional Machine Learning Engineer are highly beneficial. Strong problem-solving abilities, collaboration, and communication skills help you work effectively across data science and engineering teams. These skills are essential for reliably deploying, monitoring, and maintaining scalable machine learning solutions in production environments.

How does an MLOps machine learning engineer typically collaborate with data scientists and software engineers during the deployment of machine learning models?

An MLOps Machine Learning Engineer acts as a bridge between data scientists and software engineers, ensuring machine learning models transition smoothly from development to production. They often work closely with data scientists to understand model requirements, data pipelines, and performance metrics, while also collaborating with software engineers to integrate models into scalable systems. Regular communication, shared documentation, and joint troubleshooting sessions are common, as the role requires aligning model performance with system reliability and maintainability. This collaborative environment helps ensure that models are robust, scalable, and impactful in real-world applications.

What is the difference between Mlops Machine Learning Engineer vs Data Scientist?

AspectMlops Machine Learning EngineerData Scientist
Required CredentialsBachelor's or master's in CS, data science, or related fields; certifications in cloud platforms or MLOps toolsBachelor's or master's in statistics, data science, or related fields; certifications in data analysis or machine learning
Work EnvironmentFocus on deploying, maintaining, and scaling ML models in production environmentsFocus on data analysis, model development, and insights generation
Employer & Industry UsageTech companies, startups, enterprises implementing ML solutionsResearch institutions, analytics firms, tech companies for data insights

While both roles involve machine learning, Mlops Machine Learning Engineers specialize in deploying and maintaining models in production, ensuring scalability and reliability. Data Scientists primarily focus on developing models and analyzing data to generate insights. The roles often overlap but differ in their core responsibilities and work environments.

Are MLOps machine learning engineers in demand?

MLOps machine learning engineers are in high demand due to the increasing adoption of AI and machine learning across industries. They are needed to develop, deploy, and maintain scalable ML systems, often requiring skills in cloud platforms, automation, and tools like Docker and Kubernetes. The role offers strong job growth prospects and competitive salaries.

Do MLOps Machine Learning Engineers need a degree?

MLOps Machine Learning Engineers typically do not require a formal degree but often have a background in computer science, data science, or related fields. Practical skills in machine learning, cloud platforms, and tools like Docker, Kubernetes, and CI/CD pipelines are highly valued. Certifications and hands-on experience can also enhance job prospects.

What are popular job titles related to Mlops Machine Learning Engineer jobs in Georgia?

For Mlops Machine Learning Engineer jobs in Georgia, the most frequently searched job titles are:

What job categories do people searching Mlops Machine Learning Engineer jobs in Georgia look for?

The top searched job categories for Mlops Machine Learning Engineer jobs in Georgia are:

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

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

Mlops Machine Learning Engineer jobs near you

Machine Learning Engineer III 4P/791

Atlanta, GA

4P Consulting Inc.
Industrial Automation Equipment Manufacturing • 1 - 10 employees
Learn more about 4P Consulting Inc.external

Contractor

Posted 24 days ago

Job description

Position: Machine Learning Engineer III – AI/ML Product Engineering

Location: Atlanta, GA

Duration: 5 Months
Client: Southern Company Services

Southern Company Services is seeking an experienced Machine Learning Engineer III to develop scalable, reusable, and production-grade AI products for deployment across multiple operating companies.

This role will focus on Retrieval-Augmented Generation, multi-agent systems, natural language processing, model deployment, and cloud-based AI solutions. The ideal candidate will have strong software engineering skills, hands-on AI/ML experience, and expertise with Azure or Google Cloud Platform.

Key Responsibilities

· Design and build modular, reusable AI components and services.

· Develop scalable RAG solutions using structured and unstructured data.

· Engineer multi-agent systems for task coordination, workflow automation, and decision support.

· Build transcription and NLP pipelines for customer-interaction analysis.

· Develop and fine-tune models using PyTorch, Hugging Face Transformers, LangChain, or similar frameworks.

· Package and deploy models using Azure Machine Learning, Google Cloud Platform, or Databricks.

· Integrate Databricks for data ingestion, feature engineering, experimentation, and model development.

· Develop reusable libraries, APIs, templates, and engineering patterns.

· Partner with MLOps, DevOps, data engineering, architecture, and product teams.

· Implement monitoring for model performance, data drift, system usage, and operational reliability.

· Ensure AI solutions meet enterprise security, privacy, compliance, scalability, and observability requirements.

· Provide technical guidance to teams adopting shared AI products and components.

Required Qualifications

· Strong experience developing and deploying production-grade AI and machine learning solutions.

· Hands-on experience with RAG architectures, LLM applications, multi-agent systems, and NLP.

· Experience with Azure AI services, Google Cloud Platform AI services, or Azure Machine Learning.

· Proficiency with Python and frameworks such as PyTorch, Transformers, or LangChain.

· Experience deploying scalable models and AI services in cloud environments.

· Knowledge of APIs, software engineering practices, model monitoring, and MLOps.

· Experience working with structured and unstructured datasets.

· Strong communication, collaboration, analytical, and problem-solving skills.

Preferred Qualifications

· Experience with Databricks, vector databases, embeddings, and semantic search.

· Experience building reusable enterprise AI platforms or shared AI services.

· Knowledge of model evaluation, data drift, observability, and responsible AI.

· Familiarity with CI/CD, containers, Kubernetes, and cloud-native deployment.

· Utility, energy, or regulated-industry experience is preferred.