Machine Learning Engineer 3 4P/392

Machine Learning Engineer 3 (AI Engineer)Location: Atlanta, GAClient- Southern Comapny GasContract- 1 YearJob SummaryWe 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 ResponsibilitiesAI 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.QualificationsEducation: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 QualificationsLighting 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. Machine Learning Engineer 3 (AI Engineer)Location: Atlanta, GAClient- Southern Comapny GasContract- 1 YearJob SummaryWe 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 ResponsibilitiesAI 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.QualificationsEducation: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 QualificationsLighting 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.

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