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# Analytics and AI Integration Engineer

- **Company:** CXApp US, Inc.
- **Location:** San Ramon, CA, United States
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Amazon Web Services, Business Analytics Applications, Data Analysis, Application Integration Architecture, Microsoft Azure, Computer Programming, Monitoring of Systems, Python (Programming Language), Machine Learning, Performance Tuning, Cloud Services, Strategies of Testing, AI Platforms, Information Technology, Machine Learning Operations
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/analytics-and-ai-integration-engineer-cxapp-us-inc-7202464

## About the Role

* Bachelor's degree in Computer Science, Data Science, or a related field.
 * Proven experience as an engineer working on AI integration and analytics projects.
 * Strong understanding of AI concepts, machine learning models, and cloud services.
 * Proficiency in AI model deployment and management.
 * Knowledge of performance optimization techniques.
 * Experience with testing methodologies and tools.
 * Proficiency in programming languages like Python, Java, or relevant languages.
 * Strong problem-solving and analytical skills.
 * Excellent communication and collaboration skills.
*

## Description

As an Analytics and AI Integration Engineer at CXAPP, you will play a crucial role in integrating AI capabilities into our systems, optimizing performance, and ensuring the seamless deployment of AI models. You will work with cross-functional teams to enhance our cloud services and testing methodologies to deliver high-quality AI-driven solutions., 1. AI Integration: Collaborate with data scientists and AI developers to integrate AI models into our applications and systems, ensuring proper functionality and performance.

 2. Analytics: Implement data analytics solutions to gather insights, monitor system performance, and drive data-driven decision-making.
 3. Cloud Services: Leverage cloud platforms (e.g., AWS, Azure) to deploy and manage AI services and applications efficiently.
 4. AI Model Deployment: Deploy AI models in production environments, monitoring their performance and scalability.
 5. Performance Optimization: Optimize the performance of AI-integrated systems, including latency reduction and resource management.
 6. Testing: Develop and execute testing strategies to validate the functionality and reliability of AI and analytics solutions.
 7. Documentation: Maintain clear and comprehensive documentation of integration processes, performance optimizations, and test cases.


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