ZipRecruiter

Log In

1

Machine Learning Data Engineer Jobs in Atlanta, GA

Infinitive Inc

Data Engineer

Decatur, GA · On-site

$111K - $134K/yr

Support Big Data, Advanced Analytics, and Machine Learning use cases from a data engineering perspective * Maintain documentation for pipelines, transformations, data flows, and operational ...

Masterapp Labs

Be Seen First

Machine Learning Engineer / Data Scientist

Alpharetta, GA · On-site

$50 - $65/hr

Machine Learning Engineer / Data Scientist Location: Alpharetta , Georgia. Onsite work schedule. Client: UPS Duration: 12+ Months, extension likely for many years Interview Type: MS team interview/In ...

Masterapp Labs

Be Seen First

Machine Learning Engineer / Data Scientist

Alpharetta, GA · On-site

$50 - $65/hr

Machine Learning Engineer / Data Scientist Location: Alpharetta , Georgia. Onsite work schedule. Client: UPS Duration: 12+ Months, extension likely for many years Interview Type: MS team interview/In ...

Deloitte

AI and Data Science Engineer III

Atlanta, GA · On-site +1

$110K - $132K/yr

AI and Data Science Engineer III Position Summary Our Deloitte Human Capital team transforms ... Deliver governed datasets and feature engineering and serving patterns for machine learning ...

Cox Automotive

Machine Learning Lead Engineer

Redan, GA · On-site

$134K - $224K/yr

Analyzes complex data sets to solve real-world business and customer use cases. * Performs end-to-end development of machine learning models * May assist with or lead the development of industry ...

Cox Automotive

Machine Learning Lead Engineer

Decatur, GA · On-site

$134K - $224K/yr

Analyzes complex data sets to solve real-world business and customer use cases. * Performs end-to-end development of machine learning models * May assist with or lead the development of industry ...

next page

Showing results 1-20

All JobsMachine Learning Data Engineer JobsMachine Learning Data Engineer Jobs in Atlanta, GA

Machine Learning Data Engineer information

See Atlanta, GA salary details

$42.8K

$124.7K

$170.7K

How much do machine learning data engineer jobs pay per year?

As of Jun 23, 2026, the average yearly pay for machine learning data engineer in Atlanta, GA is $124,743.00, according to ZipRecruiter salary data. Most workers in this role earn between $110,100.00 and $132,200.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Machine Learning Data Engineer position, and why are they important?

To thrive as a Machine Learning Data Engineer, you typically need strong programming skills in Python or Scala, a deep understanding of data structures, algorithms, and machine learning concepts, as well as a degree in computer science or a related field. Experience with big data tools like Spark, Hadoop, and cloud platforms such as AWS or Azure, along with knowledge of data pipelines and ETL processes, is highly valuable; certifications in these areas can be advantageous. Problem-solving ability, attention to detail, and strong communication skills help professionals excel when working with diverse technical teams and stakeholders. These skills ensure data engineers can effectively build reliable, scalable data systems that support the development and deployment of machine learning models.

Can a data engineer become a machine learning engineer?

A data engineer can transition to a machine learning engineer role by gaining knowledge of machine learning algorithms, model development, and deployment techniques. Skills in programming languages like Python, experience with frameworks such as TensorFlow or PyTorch, and understanding of data pipelines are essential for this progression.

Which 5 jobs will survive AI?

Machine Learning Data Engineers are likely to continue to be in demand as AI advances because they develop and maintain the data pipelines and models essential for AI systems. Roles that require complex problem-solving, creativity, and human judgment, such as healthcare professionals, educators, skilled trades, and certain managerial positions, are also expected to persist despite AI automation. These jobs often involve tasks that are difficult for AI to replicate fully.

What is a Machine Learning Data Engineer job?

A Machine Learning Data Engineer is responsible for designing, building, and maintaining the data infrastructure that supports machine learning models. They develop data pipelines, ensure data quality, and optimize data storage for efficient processing. This role involves working with large-scale datasets, implementing ETL processes, and collaborating with data scientists to deploy machine learning models. Strong knowledge of databases, cloud platforms, and programming languages like Python and SQL is essential. Their work enables organizations to leverage machine learning effectively by providing reliable and scalable data solutions.

What are the typical daily responsibilities of a Machine Learning Data Engineer?

As a Machine Learning Data Engineer, your daily responsibilities often include designing, building, and maintaining data pipelines that efficiently move and transform data for machine learning applications. You may clean, preprocess, and validate large datasets, optimize storage solutions, and work closely with data scientists to ensure data is accessible and usable for model training and evaluation. Regular collaboration with software engineers and business analysts is common to align project goals and solve data-related challenges. Staying up to date with the latest tools and technologies is also important, as you'll help enable scalable and efficient deployment of machine learning solutions.

What engineers make $500,000?

Senior machine learning data engineers with extensive experience, advanced skills in data pipelines, cloud platforms, and machine learning frameworks can earn $500,000 or more annually, especially in high-cost-of-living areas or within large tech companies. Achieving this level typically requires a combination of technical expertise, leadership roles, and often stock options or bonuses.

Is ML a high paying job?

Machine Learning Data Engineers typically earn high salaries due to the specialized skills required, such as proficiency in programming, data modeling, and machine learning frameworks. Salaries vary by experience, location, and industry, but overall, the role is considered well-compensated within the tech field.
What are popular job titles related to Machine Learning Data Engineer jobs in Atlanta, GA? For Machine Learning Data Engineer jobs in Atlanta, GA, the most frequently searched job titles are:
What job categories do people searching Machine Learning Data Engineer jobs in Atlanta, GA look for? The top searched job categories for Machine Learning Data Engineer jobs in Atlanta, GA are:
Machine Learning Data Engineer jobs near you
Infographic showing various Machine Learning Data Engineer job openings in Atlanta, GA as of June 2026, with employment types broken down into 57% Full Time, 39% Part Time, 3% Contract, and 1% Nights. Highlights an 88% Physical, 5% Hybrid, and 7% Remote job distribution, with an average salary of $124,743 per year, or $60 per hour.

Machine Learning Engineer III / AI-ML Engineer

4pconsultinginc

Atlanta, GA • On-site

Contractor

Posted 18 days ago

Job description

Position:           Machine Learning Engineer III – AI/ML Engineer

Location:          Atlanta, GA

Duration:          6 Months

Client:             Southern Company services

Job Summary

We are seeking an experienced Machine Learning Engineer III / AI-ML Engineer to support the development of reusable, scalable AI products that can be deployed across multiple operating companies and business units.

This role will focus on building production-grade AI solutions, including Retrieval-Augmented Generation (RAG), multi-agent orchestration, NLP pipelines, transcription solutions, model deployment, and reusable AI components for internal operational workflows.

The ideal candidate will have strong hands-on experience with GCP or Azure AI services, modern ML frameworks, strong software engineering skills, and a product-focused mindset.

Key Responsibilities

  • Design and build modular, reusable AI components that can scale across business units.
  • Lead development of scalable RAG-based solutions for document comparison and analysis.
  • Work with structured and unstructured data to support AI-driven business solutions.
  • Engineer multi-agent systems for intelligent task coordination and decision support.
  • Develop transcription and NLP pipelines for customer interaction analysis.
  • Build, fine-tune, and deploy models using tools and frameworks such as PyTorch, Transformers, and LangChain.
  • Package models for deployment in GCP, Azure ML, and/or Databricks.
  • Integrate with Databricks for data ingestion, feature engineering, experimentation, and model development.
  • Work closely with MLOps, DevOps, and Data Engineering teams to align infrastructure and deployment patterns.
  • Contribute to shared libraries, APIs, templates, and reusable frameworks that accelerate AI product delivery.
  • Provide technical guidance to teams adopting reusable AI components.
  • Ensure AI products meet enterprise-grade security, compliance, scalability, and maintainability standards.
  • Implement monitoring for model performance, data drift, usage metrics, and production reliability.

Required Qualifications

  • Experience as a Machine Learning Engineer, AI Engineer, Data Scientist, or similar technical role.
  • Strong experience building production-grade AI/ML solutions.
  • Hands-on experience with cloud-based AI services, preferably GCP or Azure.
  • Experience developing RAG-based applications using structured and unstructured data.
  • Strong knowledge of machine learning, NLP, LLMs, and modern AI application patterns.
  • Experience with frameworks and tools such as:
    • PyTorch
    • Transformers
    • LangChain
  • Experience deploying models in cloud or enterprise environments.
  • Strong programming and software engineering skills.
  • Ability to work with APIs, reusable components, and scalable architectures.
  • Experience collaborating with MLOps, DevOps, and data engineering teams.
  • Strong analytical, problem-solving, and communication skills.

Preferred Qualifications

  • Experience with Azure ML, GCP Vertex AI, Databricks, or similar platforms.
  • Experience designing multi-agent systems or AI orchestration workflows.
  • Experience developing transcription, NLP, or customer interaction analytics pipelines.
  • Experience with model monitoring, data drift detection, observability, and usage metrics.
  • Experience building shared AI libraries, reusable templates, or internal AI platforms.
  • Understanding of enterprise security, compliance, and governance requirements for AI products.
  • Product mindset with the ability to design AI solutions that are reusable, scalable, and business-focused.