Java AI Engineer
Occupations:
Software DevelopersComputer Systems Engineers/ArchitectsComputer ProgrammersComputer Systems AnalystsWeb DevelopersIndustries:
Software PublishersComputer Systems Design and Related ServicesComputing Infrastructure Providers, Data Processing, Web Hosting, and Related ServicesEmployment ServicesEducational Support ServicesJob Title: Java AI Engineer - No OPT / H1BLocation: Sunnyvale, CA (2 Openings) / Austin, TX (3 Openings) - Locals OnlyEmployment Type: ContractExperience Level: 5-10 YearsWork Authorization: No OPT / H1BAbout the RoleWe 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 ResponsibilitiesDesign, 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. Required Skills & Experience5-10 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 TensorFlow, PyTorch, OpenCV.Strong debugging, problem-solving, and performance optimization skills.Excellent communication and collaboration skills. Nice-to-Have SkillsExperience 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).