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Applied Machine Learning Intern Jobs in Georgia (NOW HIRING)

RELX

Senior Data Scientist II

Alpharetta, GA

Experience training, finetuning, or evaluating neural network-based models as part of applied machine learning solutions. * Experience in applying software engineering best practices to data science ...

E-Solutions

ML Ops Engineer

Alpharetta, GA · On-site

Alpharetta, GA Need candidates who can work onsite form Day 1 (5 days) Financial Domain client Skills Required: • 4-8 years' experience of applied machine learning/ML Ops in BFS / Investment ...

RELX Group plc

Senior Data Scientist II

Alpharetta, GA · On-site

Experience training, fine-tuning, or evaluating neural network-based models as part of applied machine learning solutions. * Experience in applying software engineering best practices to data science ...

LexisNexis

Senior Data Scientist II

Alpharetta, GA · On-site

Experience training, fine-tuning, or evaluating neural network-based models as part of applied machine learning solutions. * Experience in applying software engineering best practices to data science ...

EMCOR Group, Inc.

Learning and Development Intern

Atlanta, GA · On-site +1

$14.50 - $19.25/hr

This internship is designed for graduate students seeking applied experience in instructional design, leadership development, learning evaluation, and organizational development. The intern will ...

Hertz

VP Applied Data & Analytics

Atlanta, GA

$310K/yr

We are seeking a strategic and hands-on Vice President, Applied Data & Analytics to lead the design ... Advanced Analytics & Machine Learning * Oversee development and deployment of predictive and ...

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Applied Machine Learning Intern information

What is the difference between Applied Machine Learning Intern vs Data Science Intern?

AspectApplied Machine Learning InternData Science Intern
Required SkillsMachine learning algorithms, programming (Python, R), data analysisStatistical analysis, data visualization, programming (Python, R)
Work EnvironmentDeveloping ML models, experimenting with algorithms, deploying modelsData cleaning, analysis, reporting insights
Industry UsageTech companies, AI startups, research labsBusiness analytics, market research, finance

Applied Machine Learning Interns focus on developing and deploying machine learning models, requiring knowledge of algorithms and programming. Data Science Interns typically handle data analysis, visualization, and reporting. While both roles involve data skills, applied ML interns work more on model implementation, whereas data science interns focus on insights and data interpretation.

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Machine Learning Engineer 3 (AI Engineer)

4pconsultinginc

Atlanta, GA • On-site

Contractor

Posted 11 days ago

Job description

Machine Learning Engineer 3 (AI Engineer)

Location: Atlanta, GA

Client- Southern Company Gas

Contract- 1 Year
 

Job Summary

We are seeking a highly skilled Machine Learning Engineer (Level 3) with 5–10 years of experience to design, develop, and deploy advanced AI models and systems. This role requires expertise in machine learning, data analysis, and model deployment to optimize business operations and drive innovation within the utilities and energy sector.

The successful candidate will collaborate with cross-functional teams—including data scientists, engineers, and business stakeholders—to integrate AI solutions into real-world applications that support operational efficiency, customer service, and sustainability initiatives.

Key Responsibilities

  • AI Model Development: Design and implement machine learning models and algorithms to address utility-specific challenges such as grid optimization, asset reliability, predictive maintenance, and customer analytics.

  • Data Analysis: Analyze large, complex datasets from SCADA, AMI, and IoT systems to extract actionable insights.

  • Model Training & Evaluation: Train, test, and validate AI models to ensure accuracy, scalability, and compliance with industry reliability standards.

  • Deployment & Integration: Deploy AI solutions into production systems and integrate with enterprise platforms (e.g., Azure, Maximo, EMS/DMS systems).

  • Innovation: Stay current with the latest advancements in AI/ML and recommend solutions that can enhance grid resilience, safety, and efficiency.

  • Collaboration: Partner with engineering, IT, and business units to define requirements and deliver business-aligned AI solutions.

  • Performance Monitoring: Continuously monitor AI models and refine as needed to maintain performance and compliance.

  • Documentation & Knowledge Sharing: Create clear documentation of models, workflows, and processes for reuse and compliance.

Qualifications

Education:

  • Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related field.

Experience:

  • 5–10 years of experience in AI, ML, or data science roles, with proven success in AI model development and deployment.

  • Industry experience in utilities, energy, or large-scale infrastructure data is preferred.

Technical Skills:

  • Proficiency in Python, R, or Java.

  • Experience with ML frameworks: TensorFlow, PyTorch, scikit-learn.

  • Strong grasp of data structures, algorithms, and applied statistics.

  • Familiarity with cloud platforms such as Azure ML and Azure Databricks (preferred), AWS or Google Cloud (a plus).

  • Experience with big data tools (e.g., Spark, Hadoop) is desirable.

  • Exposure to natural language processing (NLP) or computer vision a plus.

Soft Skills:

  • Strong analytical and problem-solving abilities.

  • Excellent communication skills for cross-functional collaboration.

  • Ability to work independently and manage multiple projects simultaneously.

  • Experience working in agile or iterative development environments.

Preferred Qualifications

  • Lighting up AI/ML use cases in the utility/energy sector (e.g., outage prediction, DERMS optimization, vegetation management analytics).

  • Certifications in AI/ML, data science, or cloud platforms (Azure, AWS, GCP).

  • Experience with MLOps pipelines and CI/CD integration for model deployment.