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Java AI Engineer
Job in
Austin, Travis County, Texas, 78716, USA
Listed on 2026-06-02
Listing for:
Kaav, Inc.
Full Time
position Listed on 2026-06-02
Job specializations:
-
Software Development
AI Engineer, Machine Learning/ ML Engineer
Job Description & How to Apply Below
Location: Sunnyvale, CA (2 Openings) / Austin, TX (3 Openings) - Locals Only
Employment Type: Contract
Experience Level: 5-10 Years
Work Authorization: No OPT / H1B
About the Role
We are seeking talented Java AI Engineers to join our development team in Sunnyvale, CA and Austin, TX. The ideal candidate will have a strong background in Java full-stack development and hands-on experience in AI/ML integration. You will collaborate with cross-functional teams to design and implement intelligent, scalable, and high-performing applications that power innovative product features.
Key Responsibilities
- Design, develop, and maintain scalable Java-based applications.
- Collaborate with cross-functional teams to integrate AI and ML components into full-stack solutions.
- Research, prototype, and implement AI-driven features to enhance product functionality.
- Participate in code reviews, testing, debugging, and deployment processes.
- Optimize applications for performance, scalability, and high availability.
- Stay updated with emerging AI/ML frameworks and best practices.
- 5-10 years of hands-on experience with Core Java and Full-Stack Java Development.
- Strong understanding of software engineering principles, design patterns, and best practices.
- 2-5 years of experience in Artificial Intelligence and Machine Learning.
- Practical experience in building or integrating ML models into enterprise solutions.
- Familiarity with AI/ML frameworks and libraries such as Tensor Flow, PyTorch, OpenCV.
- Strong debugging, problem-solving, and performance optimization skills.
- Excellent communication and collaboration skills.
- Experience with cloud platforms (AWS, GCP, or Azure) for AI/ML deployment.
- Exposure to data engineering pipelines and model lifecycle management (MLOps).
- Knowledge of microservices architecture and containerization (Docker, Kubernetes).
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