Java AI Jobs

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Translate business requirements into requirements for AI/ML models; prepare data to train and evaluate AI/ML/DL models; build AI/ML/DL models by applying state-of-the-art algorithms, especially transformers; leverage existing algorithms from academic or industrial research when applicable; test, evaluate, and benchmark the AI/ML/DL models, and publish the models, data sets, and evaluations; deploy models in production by containerizing the models; work with customers and internal employees to refine the quality of the models; establish continuous learning pipelines for models with online learning or transfer learning; build and deploy containerized applications on cloud or on-premise environments.

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Responsibilities include translating business requirements into requirements for AI/ML models, preparing data to train and evaluate AI/ML/DL models, building AI/ML/DL models using state-of-the-art algorithms especially transformers, testing and evaluating models, benchmarking quality, publishing models and datasets, deploying models in production by containerizing them, working with customers and internal employees to refine model quality, establishing continuous learning pipelines with online or transfer learning, and building and deploying containerized applications on cloud or on-premise environments.

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You will be responsible for defining operational domains and evaluating the reliability of the AI capabilities developed in-house. You will develop and extend the state-of-the-art in uncertainty quantification and uncertainty calibration. This will involve understanding the AI systems built, interfacing with them, and evaluating their robustness in real-world and adversarial scenarios. You will contribute to impactful projects and collaborate with people across several teams and backgrounds.

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The role involves defining operational domains and evaluating the reliability of AI capabilities developed in-house. Responsibilities include developing and extending methods for uncertainty quantification and uncertainty calibration, understanding the AI systems built by the company, interfacing with these systems, and evaluating their robustness in real-world and adversarial scenarios. The position requires contributing to impactful projects and collaborating with people across multiple teams and backgrounds.

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Testing of AI based conversational products; Monitoring and improving quality assurance process ensuring any agreed-upon standards and procedures are followed; Providing a high level of data quality awareness across multiple teams; Evaluating and identifying where enhancements in accuracy of models are required; Detailed testing feedback preparation to help the team to improve AI models.

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Translate business requirements into requirements for AI/ML models, prepare data to train and evaluate AI/ML/DL models, build AI/ML/DL models by applying state-of-the-art algorithms including transformers and leveraging existing algorithms from academic or industrial research, test and evaluate AI/ML/DL models, benchmark their quality, and publish the models, data sets, and evaluations, deploy models in production by containerizing them, work with customers and internal employees to refine model quality, establish continuous learning pipelines for models using online learning or transfer learning, and build and deploy containerized applications on cloud or on-premise environments.

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Design and build extensible backend systems supporting flexible configurations for different customers and content types. Develop infrastructure interfacing with LLMs to enable prompt engineering, context injection, and modular evaluation workflows. Build tooling and platforms for fast iteration by AI engineers and analysts, including declarative pipelines, parameterized jobs, and reproducible experiments. Prioritize ease of deployment, integration, and testing for internal teams and external partners. Collaborate closely with product, data, and policy teams to translate nuanced safety needs into scalable, maintainable software systems.

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You will be responsible for defining operational domains and evaluating the reliability of the AI capabilities developed in-house. You will develop and extend the state-of-the-art in uncertainty quantification and uncertainty calibration. This will involve understanding the AI systems we build, interfacing with them, and evaluating their robustness in real-world and adversarial scenarios. You will contribute to impactful projects and collaborate with people across several teams and backgrounds.

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Translate business requirements into requirements for AI/ML models; prepare data to train and evaluate AI/ML/DL models; build AI/ML/DL models by applying state-of-the-art algorithms, especially transformers; leverage existing algorithms from academic or industrial research when applicable; test, evaluate, and benchmark AI/ML/DL models and publish the models, data sets, and evaluations; deploy models in production by containerizing them; work with customers and internal employees to refine model quality; establish continuous learning pipelines for models using online or transfer learning; build and deploy containerized applications on cloud or on-premise environments.

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The AI Factory Customer Engineer is responsible for translating business requirements into AI/ML model requirements, preparing data to train and evaluate AI/ML/DL models, building AI/ML/DL models using state-of-the-art algorithms, particularly transformers, and leveraging existing algorithms from research. They test and evaluate the models, benchmark their quality, and publish models, datasets, and evaluations. This role includes deploying models in production through containerization, working with customers and internal teams to refine model quality, establishing continuous learning pipelines for models with online or transfer learning, and building and deploying containerized applications on cloud or on-premise environments.