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Executive Google Cloud Machine Learning Engineer Jobs in Georgia

Equifax, Inc.

Machine Learning Engineer

Atlanta, GA · On-site

... executives, customers, and peers from other businesses and institutions * Contribute to all phases ... Cloud Certification Strongly Preferred What could set you apart * Application Development ...

Five and Fly

Machine Learning Engineer

Atlanta, GA · On-site

Google Gemini (multimodal reasoning, advanced RAG integration). * Meta LLaMA (fine-tuned/custom ... Leverage cloud ML platforms (AWS Sagemaker, Databricks ML) for experimentation and scaling.

KSB GIW, Inc.

Machine Learning Engineer

Grovetown, GA · On-site

$70 - $110/hr

Machine Learning Engineer Department: Engineering, Research & Development Reports to: Metallurgical ... Cloud compute (AWS or Azure) and GPU‑based training * Coursework or research projects in ...

Speria

Machine Learning Engineer

Atlanta, GA · On-site

$120 - $160/hr

... and executive management. Job Summary We are seeking a highly skilled and motivated Machine ... Feature engineering pipelines for Machine Learning * Git, CI/CD, reproducible workflows

KSB, Inc.

Machine Learning Engineer

Grovetown, GA · On-site

$70 - $95/hr

Machine Learning Engineer**### **KSB GIW, Inc.**### **Department:** Engineering, Research ... Cloud compute (AWS or Azure) and GPU-based training* Coursework or research projects in numerical ...

Ksb

Machine Learning Engineer

Grovetown, GA · On-site

Machine Learning Engineer KSB GIW, Inc. Department: Engineering, Research & Development Reports to ... Cloud compute (AWS or Azure) and GPU-based training * Coursework or research projects in numerical ...

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Machine Learning Engineer III 4P/791

4P Consulting Inc.

Atlanta, GA • On-site

Contractor

Posted 25 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.