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Machine Learning Nlp Jobs in Georgia (NOW HIRING)

Athena Technology Group

Experience with supervised and unsupervised machine learning, NLP, and network analysis * Experience maintaining and deploying JupyterHub or similar environments * Strong background in mathematics ...

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 ... NLP, or information retrieval • Familiarity with data pipelines, feature stores, or embedding ...

StackandCo

$48.75 - $62.25/hr

They are creating something pretty amazing and are getting their hands dirty in Machine Learning \/ NLP. \n \n \n \n \n \n This is essentially a backend development role, therefore someone who has ...

Iterable

Senior Machine Learning Engineer (Nova)

Atlanta, GA · On-site

$100K - $138K/yr

They are seeking a Senior Machine Learning Engineer to build core Machine Learning foundations ... NLP. • Experience working in remote-first engineering teams. Company : Iterable is an AI-powered ...

Workday

Sr Machine Learning Engineer

Atlanta, GA · On-site

$159K - $276K/yr

Knowledge of deep learning and NLP techniques * Experience building Extract Transform Load (ETL) ... for machine learning services * Experience in analyzing the ML algorithms that could be used to ...

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$31.7K

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$165.9K

How much do machine learning nlp jobs pay per year?

As of Jul 7, 2026, the average yearly pay for machine learning nlp in Georgia is $103,638.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,200.00 and $114,800.00 per year, depending on experience, location, and employer.

What is a Machine Learning NLP job?

A Machine Learning NLP job involves developing algorithms and models that enable machines to understand, process, and generate human language. Professionals in this role work with large datasets, train models on text data, and fine-tune natural language processing techniques such as sentiment analysis, text classification, and language translation. They often use machine learning frameworks like TensorFlow, PyTorch, and NLP libraries such as spaCy or Hugging Face Transformers. The goal is to build intelligent applications, including chatbots, search engines, and automated content analysis systems.

What are the typical daily responsibilities of a Machine Learning NLP specialist?

As a Machine Learning NLP specialist, your daily responsibilities often include designing and implementing NLP models, cleaning and preprocessing large text datasets, and experimenting with algorithms to improve model performance. You may also evaluate model results, collaborate with software engineers and data scientists, and stay updated on the latest research in the field. Frequent code reviews, participation in team meetings, and contributing to documentation are also common. This role combines hands-on technical work with collaborative problem-solving to develop language-based AI solutions for real-world applications.

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

To thrive as a Machine Learning NLP professional, you need a strong background in machine learning, natural language processing, data analysis, and proficiency in programming languages such as Python, typically supported by a relevant degree in computer science or related field. Familiarity with NLP libraries (like spaCy, NLTK, or Hugging Face), machine learning frameworks (such as TensorFlow or PyTorch), and experience with cloud platforms are highly valued, and certifications can enhance your profile. Strong problem-solving skills, effective communication abilities, and adaptability are important soft skills in this role. These competencies enable you to build sophisticated language models and efficiently collaborate on cross-functional projects in a rapidly evolving technical landscape.

What are popular job titles related to Machine Learning Nlp jobs in Georgia? For Machine Learning Nlp jobs in Georgia, the most frequently searched job titles are:
What job categories do people searching Machine Learning Nlp jobs in Georgia look for? The top searched job categories for Machine Learning Nlp jobs in Georgia are:
Machine Learning Nlp jobs near you
Infographic showing various Machine Learning Nlp job openings in Georgia as of July 2026, with employment types broken down into 100% Contract. Highlights an 100% In-person job distribution, with an average salary of $103,638 per year, or $49.8 per hour.

Machine Learning Engineer III / AI-ML Engineer

4pconsultinginc

Atlanta, GA • On-site

Contractor

Re-posted 3 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.