101 to 125 of 666 Continuous Integration Jobs

in production. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed More ❯

in production. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed More ❯

in production. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed More ❯

in production. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed More ❯

thread, and advanced analytics across weapon system design, manufacturing, and sustainment. Oversee the design, implementation, and maintenance of scalable data infrastructure, to include data creation/collection, storage, management, integration and analysis, and data serving through modern technology. Lead and govern the use of the Program data infrastructure and capabilities to ensure seamless data flow and traceability across engineering … presenting solutions to senior leadership. Experience in leading organizational change and ensuring successful adoption of new technologies and best practices. Experience mentoring junior engineers and fostering a culture of continuous improvement. Proven ability to build and lead high-performing teams, including recruitment, performance management, and professional development. Ability to strategically allocate resources and manage budgets to optimize team performance … understanding of data lakes, warehouses, and/or streaming platforms. Data Engineering experience with tooling, such as Apache Spark and Kafka, and orchestration tools like Apache Airflow or equivalent. Continuous Integration/Continuous Deployment experience with CI/CD tools like Jenkins or GitLab tailored for data pipelines. Cloud Expertise experience and/or knowledge in cloud More ❯

Appreciation of data architecture principles. Experience working with: - Cloud Native design patterns, with a preference towards Microsoft Azure/Google Cloud - Experience with interface implementation between SaaS providers (leveraging Integration-as-a-service frameworks). - Experience supporting digital platforms, including Integrations, Release Management, Regression Testing, Integrations, Data Obfuscation, etc. - Experience scaling an "API-Ecosystem", designing, and implementing "API-First … integration patterns. Strong organizational and communications skills with multiple examples of being able to convey complex technical topics, that resulted in a definitive direction. Strong leadership and proactive communication skills with a strong bias for action and ability to advise others. What will give you a competitive edge (preferred qualifications): Experience working with: - Modern application architecture methodologies (Service Orientated … Architecture, API-Centric Design, Twelve-Factor App, FAIR, etc.) - "DevSecOps" culture, including modern software development practices, covering Continuous Integration and Continuous Delivery (CI/CD), Test-Driven Development (TDD), etc. - "Deep understanding of the data lifecycle and computational workflows in a core R&D domain (e.g., target identification, lead optimization, bioinformatics, computational biology)." - "Practical experience designing More ❯

skills and knowledge. Experience working in a fast moving agile environment. Ability to work effectively in a cross-functional team environment, demonstrating a proactive and collaborative approach. Familiarity with continuous integration/continuous deployment (CI/CD) practices and tools. Benefits What will you get? Competitive salary Performance based bonus 25 days annual leave Health Insurance Company More ❯

supporting the service in production. Always be on the lookout for ways we can improve our product, processes and practices. We don’t like friction and waste. Automated testing, continuous integration and continuous deployment. We are huge proponents of automation. Working outside of your specialism when needed. While all our team members have a specialism, we don More ❯

in production. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed More ❯

in production. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed More ❯

in production. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed More ❯

in production. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed More ❯

in production. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed More ❯

in production. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed More ❯

in production. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed More ❯

in production. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed More ❯

in production. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed More ❯

in production. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed More ❯

in production. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed More ❯

in production. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed More ❯

in production. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed More ❯

in production. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed More ❯

in production. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed More ❯

in production. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed More ❯

in production. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed More ❯