AWS Python Cloud Application Architecture
J
JobgetherFinancial Services
BrazilFull-Time
Salary not disclosed
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Job Details
- Required Skills
- AWSPythonAngularServerlessTerraformDatadogGenerative AI
Requirements
- Proven experience designing and implementing cloud-native enterprise applications on AWS.
- Strong hands-on expertise with serverless and containerized architectures, particularly AWS Lambda, Step Functions, ECS/EKS Fargate, and related services.
- Solid experience designing APIs using API Gateway and OpenAPI 3.x, including authentication, versioning, and real-time communication patterns.
- Strong understanding of event-driven architectures and AWS services such as SNS, SQS, and EventBridge.
- Experience with distributed systems and resilience patterns, including sagas, eventual consistency, circuit breakers, idempotency, retries, and fault tolerance.
- Strong Python development skills and familiarity with Angular.
- Experience incorporating Generative AI into production applications, including agents, Amazon Bedrock, RAG, guardrails, and model or solution evaluation.
- Strong understanding of AWS security and IAM, including least-privilege principles and RBAC/ABAC.
- Experience with observability practices and tools such as CloudWatch and Datadog, including SLI/SLO monitoring.
- Hands-on knowledge of Terraform, Infrastructure as Code, and CI/CD practices.
Responsibilities
- Design and implement resilient, scalable, and cost-efficient enterprise applications using AWS serverless and containerized services.
- Develop solutions using AWS Lambda, Step Functions, ECS/EKS Fargate, and related cloud-native technologies.
- Design architectures that address resilience, concurrency, retries, dead-letter queues, fault tolerance, and operational efficiency.
- Create contract-first APIs using API Gateway and OpenAPI 3.x, including versioning, authentication, and real-time communication patterns.
- Design and implement event-driven architectures using SNS, SQS, and EventBridge, with appropriate approaches to ordering, idempotency, low latency, and reliability.
- Develop distributed-system patterns including sagas, eventual consistency, circuit breakers, and other resilience mechanisms.
- Incorporate Generative AI capabilities into production applications, including AI agents, Amazon Bedrock, RAG, guardrails, and quality evaluation.
- Apply application security best practices, including least-privilege access, IAM, RBAC, and ABAC.
- Establish strong operational observability using tools such as CloudWatch and Datadog, supported by appropriate SLI/SLO practices.
- Implement infrastructure and application delivery through Infrastructure as Code, Terraform, and CI/CD pipelines.
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