About the job
Join our Applied AI Solutions team and help build the secure data foundation that powers AWS's AI-driven business applications. We own critical infrastructure including event-based data stores, machine learning training platforms, and data access interfaces that enable responsible AI development while maintaining the highest standards of privacy, security, and compliance. Our platform serves as the backbone for storing, processing, and managing data that fuels AI-powered capabilities across AWS, ensuring customers retain full control over their information.
Responsibilities
- Own and deliver complete software features for event-based data storage systems, from design through deployment and operations, ensuring they provide flexible, secure, and compliant platforms for AI-powered business applications
- Maintain, refactor, and enhance existing storage and analytics platform, identifying opportunities to improve performance, scalability, and operational excellence while eliminating technical debt
- Design and implement software solutions for data access interfaces and authorization systems within established architectural strategies, seeking guidance from senior engineers when facing complex technical tradeoffs
- Mentor team members through code reviews, documentation, and knowledge sharing—providing meaningful feedback, training newcomers on system architecture, and facilitating technical discussions that promote operational excellence
- Resolve operational issues by identifying root causes and implementing permanent fixes for compliance features, data deletion APIs, and customer opt-out mechanisms, going beyond quick workarounds
- Work directly with business application teams and stakeholders to understand requirements, translating them into technical solutions that balance customer needs with platform sustainability
- Take ownership of security implementations including encryption, access controls, audit trails, and PII handling, ensuring software can be maintained and extended by other engineers
- Drive improvements to self-service platforms and control planes, making priority tradeoffs between new feature development and operational work while maintaining governance standards
Qualifications
Minimum
3+ years of non-internship professional software development experience
3+ years of non-intternship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
Experience with full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations
Experience designing and implementing data storage systems, ETL pipelines, or machine learning infrastructure for model training and evaluation workflows
Proven track record building distributed systems with event-driven architectures and APIs that operate reliably at enterprise scale
Experience with data security, privacy compliance, and implementing data retention/deletion mechanisms
Working knowledge of AWS services for data processing and machine learning such as S3, SageMaker, and related data infrastructure
Preferred
5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
Bachelor's degree in computer science or equivalent
Successful history of cross-team collaboration, driving technical convergence while maintaining delivery velocity
Experience with distributed machine learning training platforms and workflow orchestration systems
* Production experience implementing data access controls, authorization systems, and multi-tenant data isolation
Knowledge of PII handling, data pseudonymization, and privacy-preserving techniques
Experience building self-service platforms and control planes for data platform management
Familiarity with streaming data ingestion, real-time processing, and efficient querying at scale
Experience with data lineage tracking, audit trails, and compliance reporting systems