Industrial AI Engineer

Siemens•Fort Worth, TX

17d•$83,966 - $143,942•Hybrid

About The Position

Siemens is seeking an Industrial AI Engineer in the Dallas–Fort Worth area to develop and scale Industrial AI solutions that improve manufacturing efficiency, productivity, quality, and reliability. The role works with data, platform, and factory teams to turn AI concepts into production-ready solutions, validate them in live industrial environments, and support adoption on the shop floor. The position applies data, AI, and automation to real-world industrial challenges and supports repeatable deployment across manufacturing operations in the United States, Canada, and Mexico. It includes developing and deploying models, working with shop-floor time-series and MES/IT data, preparing and contextualizing data, integrating models into factory systems and workflows, and refining solutions using production data and feedback. The engineer also leads scaling efforts across plants and contributes to standardized Industrial AI capabilities.

Requirements

Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
At least 2 years of experience developing and deploying AI/ML models in production environments, preferably in industrial, manufacturing, or automation domains.
At least 2 years of experience programming in relevant languages such as Python, R, or Java and using common AI/ML frameworks such as TensorFlow, PyTorch, or scikit-learn, including hands-on model building and testing.
At least 2 years of experience deploying and validating AI models in industrial or production environments.
At least 2 years of experience working in cross-functional, customer-facing teams and adapting solutions to evolving requirements.
At least 2 years of experience working with data-driven systems in operational or industrial environments.
Willingness and ability to travel up to 20% to work with customer teams during critical deployments.
Authorized to work in the United States without sponsorship now or in the future.

Nice To Haves

Strong analytical, problem-solving, and communication skills, including the ability to translate complex technical concepts into practical industrial applications.
Experience in production manufacturing environments, including piloting, validating, and refining AI solutions based on production feedback.
Familiarity with Industrial AI platforms and ecosystems, including Siemens Senseye or Industrial Edge, or comparable AI/IoT platforms.
Familiarity with industrial automation systems, OT/IT integration, and cloud-based AI deployment.
Knowledge of data preparation, feature engineering, AI model evaluation, and compliance, safety, and regulatory requirements for AI in industrial environments.
Experience managing multiple projects and workstreams in a deadline-driven environment and supporting ongoing improvement through performance monitoring, model drift mitigation, model refinement, and scaling across deployments.
Ability to share deployment learnings and collaborate with factory teams to improve and drive adoption of AI solutions.

Responsibilities

Develop and deploy AI models to address industrial and manufacturing challenges through data-driven insights and decision-making.
Work with time-series shop-floor data and MES/IT data to design AI solutions aligned with production processes and operational needs.
Perform data preparation and feature engineering, and develop models using contextualized industrial data.
Deploy AI models in factory environments and ensure reliable performance amid data variability, latency, and operational workflow integration.
Collaborate with Cybersecurity, Data Architects, System Architects, software developers, and factory teams to build scalable, reusable solutions integrated with industrial systems.
Engage and collaborate with external partners to implement standardized Industrial AI solutions.
Validate and refine AI models using production data and feedback, targeting measurable operational and business improvements.
Lead the scaling of AI solutions across plants, establish repeatable deployment patterns, and contribute to standardizing Industrial AI capabilities.