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# Senior Python Engineer / AI Platform Engineer

- **Company:** American CyberSystems
- **Location:** Charlotte, NC, United States
- **Experience:** Expert
- **Salary:** $145,600.0 - $156,000.0
- **Contract:** Permanent contract
- **Skills:** .NET Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Application Frameworks, Microsoft Azure, C Sharp (Programming Language), Cloud Engineering, Python (Programming Language), Tensorflow, Software Engineering, Management of Software Versions, Enterprise Application Integration, Pytorch, Large Language Models, Multi-Agent Systems, Generative AI, AI Platforms, Scikit Learn, Infrastructure Automation Frameworks, Information Technology, Data Management, Machine Learning Operations, Data Pipelines, Microservices
- **Published:** September 2, 2026
- **Apply:** https://public-rest34.bullhornstaffing.com/rest-services/2WXP1S/query/JobBoardPost?where=id=1023687&fields=id,title,publishedCategory(id,name),address(city,state),employmentType,dateLastPublished,publicDescription,isOpen,isPublic,isDeleted

## About the Role

* Bachelor's or master's degree in computer science, Engineering, Data Science, or a related field.
 * Software engineering experience with expertise in Python.
 * Experience deploying AI/ML and Generative AI solutions into production environments.
 * Experience with .NET / C# development and enterprise application integration.
 * Experience in cloud platforms such as Azure, AWS, or GCP.
 * Experience with AI/ML frameworks such as PyTorch, TensorFlow, Scikit-learn, LangChain, or Semantic Kernel.

## Description

* Design, develop, and deploy scalable AI/ML and Generative AI solutions from prototype to production.
 * Collaborate with Data Scientists, ML Engineers, and business stakeholders to operationalize AI models.
 * Build and maintain robust data pipelines, APIs, and AI services using Python and .NET technologies.
 * Develop and optimize cloud-native AI platforms and microservices architectures.
 * Implement MLOps practices, including model deployment, monitoring, versioning, and automation.
 * Integrate Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, and AI orchestration frameworks.
 * Ensure solution scalability, reliability, security, and performance in production environments.
 * Create reusable frameworks, tools, and best practices for AI application development.
 * Support CI/CD pipelines and infrastructure automation for AI and data platforms.


## Related Videos

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- [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding)
- [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs)
- [AI-Augmented DevOps with Platform Engineering](https://www.wearedevelopers.com/videos/1614-ai-augmented-devops-with-platform-engineering)
- [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models)

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