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Model Predictive Control Jobs in Georgia (NOW HIRING)

QGenda

Senior Data Engineer

Atlanta, GA · Hybrid

$101K - $138K/yr

Design and refine data models and semantic layers that support analytical self-service and advanced ... Apply best practices for version control, documentation, CI/CD, Infrastructure as Code, and data ...

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Model Predictive Control information

What is model predictive control?

Model Predictive Control (MPC) is an advanced method of process control that uses a mathematical model to predict and optimize the future behavior of a system. It works by solving an optimization problem at each control step to determine the best sequence of control actions, taking into account system constraints and objectives. MPC is widely used in industries such as chemical processing, energy, and automotive because it can handle multivariable control problems and anticipate future events. Its predictive nature allows for improved performance, stability, and efficiency compared to traditional control methods.

What are the typical challenges faced by engineers working with model predictive control systems in an industrial setting?

Engineers working with Model Predictive Control systems often encounter challenges related to model accuracy, computational demands, and real-time implementation. Ensuring the process model accurately represents the plant dynamics is critical, as discrepancies can lead to suboptimal control performance. Additionally, MPC algorithms can be computationally intensive, particularly for large-scale or fast processes, requiring careful tuning and optimization to maintain real-time operation. Collaboration with process engineers and IT specialists is common, as integrating MPC with existing control systems and plant infrastructure is a key part of the role.

What are the key skills and qualifications needed to thrive as a model predictive control engineer, and why are they important?

To thrive as a Model Predictive Control Engineer, you need strong foundations in control theory, applied mathematics, and process engineering, usually supported by a degree in engineering or a related field. Proficiency with simulation tools such as MATLAB/Simulink, programming languages like Python or C++, and familiarity with industrial automation systems are typically required. Analytical thinking, problem-solving abilities, and effective communication skills help distinguish top performers in this role. These skills are essential for designing, implementing, and optimizing advanced control algorithms that improve system performance and reliability in complex industrial environments.

What is the difference between Model Predictive Control vs Control Systems Engineer?

AspectModel Predictive ControlControl Systems Engineer
CredentialsEngineering degree, control theory, process modelingEngineering degree, control systems, automation
Work EnvironmentIndustrial automation, process control, manufacturingDesign, develop, and maintain control systems across industries
Industry UsageProcess industries, chemical, oil & gas, manufacturingAutomation, robotics, embedded systems, industrial sectors

Model Predictive Control (MPC) focuses on advanced control algorithms for optimizing processes, while Control Systems Engineers design and implement various control systems. MPC is a specialized skill within control engineering, often requiring knowledge of process modeling and optimization, whereas Control Systems Engineers have broader responsibilities across multiple control technologies. Both roles are essential in industrial automation but differ in scope and application.

What does a model predictive control do?

A Model Predictive Control (MPC) engineer designs control systems that use a mathematical model to predict future system behavior and optimize control actions accordingly. MPC is commonly used in industries like process control and robotics, requiring skills in control theory, programming, and system modeling. The role involves developing algorithms, tuning controllers, and ensuring system stability and efficiency.

What are popular job titles related to Model Predictive Control jobs in Georgia?

For Model Predictive Control jobs in Georgia, the most frequently searched job titles are:

What job categories do people searching Model Predictive Control jobs in Georgia look for?

The top searched job categories for Model Predictive Control jobs in Georgia are:

What cities in Georgia are hiring for Model Predictive Control jobs?

Cities in Georgia with the most Model Predictive Control job openings:

Model Predictive Control jobs near you
Infographic showing various Model Predictive Control job openings in Georgia as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 18% Part Time, 2% Contract, and 1% Nights. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution.

MS Dynamics 365 AI Developer

Atlanta, GA • On-site

Ztek Consulting INC
See more jobs at Ztek Consulting INCexternal

Contractor

Re-posted 24 days ago

Job description

Job Title: MS Dynamics 365 AI Developer

Location: Atlanta, GA

Position Type: 6 / 12 Month Contract

Job Description

Dynamics 365 AI Developer

Dynamics 365 CE Development

  • Design, develop, and maintain custom components such as plugins, workflows, Power Automate flows, and JavaScript for D365 CE modules.
  • Implement minor enhancements and bug fixes based on business requirements and AMS priorities.
  • Collaborate with Business Analysts to translate functional requirements into technical solutions.
  • Perform code reviews, unit testing, and deployment activities across environments.

Power Platform & Copilot Studio

  • Build Power Apps (Canvas, Model-driven, Portals) and automate workflows using Power Automate.
  • Develop and deploy Copilot Studio solutions, including intelligent chatbots and virtual assistants with NLU/NLG capabilities.
  • Integrate Copilot Studio with Power Platform components and external systems; manage custom topics, entities, and actions.
  • Use AI Builder for predictive models, form processing, and automation scenarios.

Integration & Architecture

  • Integrate solutions with enterprise systems via REST APIs, Azure Functions, Logic Apps, and Dataverse.
  • Ensure compliance with security, governance, and performance standards across environments.

DevOps & Lifecycle Management

  • Manage CI/CD pipelines using Azure DevOps and follow ALM best practices.
  • Maintain technical documentation and support release cycles.

Required Skills:

Technical Expertise

  • 5+ years of hands-on experience with Microsoft Dynamics 365 CE and Power Platform.
  • Proficiency in C#, .NET, JavaScript, and Dynamics SDK.
  • Strong knowledge of Copilot Studio, conversational AI design, and AI Builder.
  • Familiarity with solution packaging, deployment, and version control.

Certifications (Preferred)

  • Microsoft Certified: Power Platform Developer Associate (PL-400).
  • Microsoft Certified: Dynamics 365 Developer Associate.
  • PL-600 or MB-600 for solution architecture roles.

Nice-to-Have:

  • Experience with PCF controls, Power Pages, and advanced AI integrations .
  • Knowledge of Azure AI Services, AI Foundry, and modern data integration frameworks.
  • Familiarity with ITIL processes and Agile delivery methodologies.