Job Summary
This hybrid role is for a senior developer with six to nine years of experience who specializes in Kubeflow machine learning and artificial intelligence solutions. The role focuses on designing building and optimizing scalable data and model pipelines that power intelligent products and enterprise applications. The position supports day time work hours with no travel expected.
Responsibilities
Design end to end Kubeflow pipelines that automate data ingestion model training validation and deployment to ensure reliable delivery of machine learning capabilities across environmentsDevelop modular reusable and secure machine learning services that integrate with existing enterprise systems to deliver consistent and scalable artificial intelligence outcomesBuild robust feature engineering workflows that transform raw data into high quality features improving model accuracy stability and long term maintainabilityOptimize training and inference workloads by tuning hyperparameters resource allocation and parallelization strategies to achieve efficient utilization of compute resourcesImplement monitoring strategies for models in production including tracking drift performance anomalies and data quality issues to maintain trustworthy machine learning outcomesCollaborate with data engineers product partners and quality specialists to translate business needs into well defined machine learning solutions that create measurable valueDocument technical designs model architectures and operational runbooks in a clear and structured manner to support knowledge sharing and operational continuityEnhance existing artificial intelligence solutions by refactoring code improving test coverage and applying modern design patterns to strengthen reliability and resilienceIntegrate security and privacy considerations into all machine learning workflows by applying access controls encryption patterns and safe data handling standardsCoordinate with platform teams to configure Kubeflow components storage backends and networking setups that enable stable hybrid deployment and smooth developer experiencesCreate automated testing suites for data pipelines models and services to detect regressions early and ensure consistent behavior across multiple environmentsGuide peers by sharing best practices in machine learning experimentation documentation and coding standards to uplift the overall quality of the technical ecosystemAlign daily development activities with organizational goals by prioritizing work that improves customer outcomes business efficiency and positive societal impact through responsible artificial intelligence useQualifications
Require professional experience of six to nine years in software development with at least three years focused on production grade machine learning and artificial intelligence solutionsRequire hands on expertise in Kubeflow including pipeline authoring component customization and integration with container orchestration platforms in hybrid environmentsRequire strong proficiency in a programming language commonly used for machine learning such as Python including experience with testing frameworks code quality tools and package managementRequire proven experience building and deploying models using machine learning frameworks such as Tensorflow or similar libraries covering both training and inference workflowsRequire solid understanding of data structures algorithms and distributed computing concepts to design efficient pipelines and scalable artificial intelligence servicesNice to have experience with cloud native services for data processing and model deployment that complement Kubeflow based solutions in large enterprisesNice to have familiarity with responsible artificial intelligence concepts including fairness transparency and explainability practices applied to real world machine learning systemsNice to have experience collaborating in hybrid work models using modern collaboration tools version control workflows and continuous integration practicesCertifications Required
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