- Contribute to technical standards and code quality through design reviews,
documentation, and peer code reviews
- Leverages enterprise-authorized AI coding assist tools within the work
environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
- Applies knowledge of tools within the Software Development Life Cycle
toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 3+
years applied experience
- Hands-on experience building and operating production large language model
applications, including agentic patterns and tool integrations
- Strong software engineering fundamentals with ability to deliver cloud-native
services using containers and serverless designs on AWS
- Advanced python programming skills with experience writing production quality
code
- Experience with retrieval-augmented generation approaches, including
embeddings and semantic search, and practical context engineering
- Proficiency building React based front end experience that integrates backend
API and provides strong attention to reliability, security, and performance
- Experience establishing or contributing to evaluation, testing, and
monitoring practices for AI system quality and reliability
- Ability to troubleshoot complex issues across distributed systems, including
asynchronous workflows and event-driven architecture
- Strong collaboration skills with the ability to communicate technical
decisions and trade-offs clearly to partners
- Hands-on experience using enterprise-authorized AI-assisted software
development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
- Understanding of responsible AI use in engineering workflows, including data
sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.