- Execute creative software solutions through design, development, and
troubleshooting to solve complex technical problems.
- Deliver end-to-end AWS cloud-native, microservices-based applications using
Python and modern engineering best practices.
- Design, build, and evolve distributed systems on AWS using Lambda,
ECS/Fargate, and/or EKS aligned to scalability, performance, and cost needs.
- Drives team adoption of enterprise-authorized AI-assisted engineering
practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
- 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.
- Apply domain modeling to structure business products for clarity,
testability, and continuous evolution.
- Develop secure, high-quality production code; perform thorough code reviews;
debug and remediate issues in others’ code.
- Build and maintain AWS infrastructure with IaC (Terraform/CloudFormation) for
repeatable, auditable deployments.
- Implement/enhance CI/CD with automated testing and reliable release patterns
(blue/green, canary, rollback) for zero-downtime where required.
- Build resiliency + observability (multi-AZ, decoupling, retries/timeouts,
idempotency, circuit breakers; logging/metrics/tracing/alerting via CloudWatch/X-Ray/OpenTelemetry).
- Operate and improve production: investigate incidents, lead RCA, prevent
recurrence by automating remediation, optimize data stores (Aurora/RDS, DynamoDB, S3), and modernize dependencies while collaborating across product/platform/SRE/security/data and mentoring inclusively.