AWS Developer
Mandatory Skills:
8 to 12 years of overall experience with strong AWS development background.
Hands-on development using AWS serverless services: Lambda, DynamoDB,
SNS/SQS, Step Functions, API Gateway.
Experience developing AI/LLM features using Amazon Bedrock or SageMaker.
Strong programming experience in Node.js or Python.
Solid experience with AWS SDK, event-driven patterns, and microservices.
Good understanding of AWS compute, networking, security, and CI/CD.
Experience with CloudFormation for IaC.
Experience with Git and modern development workflows.
Strong debugging, problem-solving, and performance optimization skills.
Ability to write secure, scalable, production-grade cloud applications.
Job Description:
To succeed, this Role's Responsibilities Would Involve:
Bring a developer's mindset with strong knowledge of AWS cloud engineering.
Be comfortable building, debugging, and enhancing serverless applications.
Work collaboratively with architects, product teams, and DevOps to deliver end-to-end
solutions.
Understand modern cloud design principles, security practices, and automation standards.
Stay current with evolving AWS services and Generative AI capabilities.
Responsibilities:
Application Development & Integration:
Build and enhance cloud-native applications using AWS services such as Lambda, API Gateway,
DynamoDB, SQS/SNS, Step Functions, and S3.
Develop AI-enabled features using LLMs, Agentic AI, and AWS services like Amazon Bedrock
and SageMaker.
Write clean, maintainable, secure, and testable code using Node.js or Python.
Implement event-driven workflows, asynchronous integrations, and serverless APIs.
Integrate AWS services programmatically using AWS SDK.
Cloud Engineering & Infra Automation:
Implement Infrastructure-as-Code (IaC) using CloudFormation.
Contribute to CI/CD pipelines using AWS Code Pipeline, Code Build, and Code Deploy.
Support observability by configuring logging, monitoring, tracing, and alerts.
Follow cloud security, compliance, and operational best practices.
AI/LLM Feature Implementation:
Build LLM-driven features, inference workflows, prompt-handling, and agent-based
automation using Bedrock.
Develop scalable AI components and integrate them into backend services.
Experiment with new AWS AI capabilities and contribute to PoCs.
Collaboration & Delivery:
Work with architects and senior engineers to translate high-level architecture into
implementation.
Participate in code reviews, design discussions, and team development practices.
Troubleshoot issues, optimize application performance, and support deployment
activities.
8 to 12 years of overall experience with strong AWS development background.
Hands-on development using AWS serverless services: Lambda, DynamoDB,
SNS/SQS, Step Functions, API Gateway.
Experience developing AI/LLM features using Amazon Bedrock or SageMaker.
Strong programming experience in Node.js or Python.
Solid experience with AWS SDK, event-driven patterns, and microservices.
Good understanding of AWS compute, networking, security, and CI/CD.
Experience with CloudFormation for IaC.
Experience with Git and modern development workflows.
Strong debugging, problem-solving, and performance optimization skills.
Ability to write secure, scalable, production-grade cloud applications.
Job Description:
To succeed, this Role's Responsibilities Would Involve:
Bring a developer's mindset with strong knowledge of AWS cloud engineering.
Be comfortable building, debugging, and enhancing serverless applications.
Work collaboratively with architects, product teams, and DevOps to deliver end-to-end
solutions.
Understand modern cloud design principles, security practices, and automation standards.
Stay current with evolving AWS services and Generative AI capabilities.
Responsibilities:
Application Development & Integration:
Build and enhance cloud-native applications using AWS services such as Lambda, API Gateway,
DynamoDB, SQS/SNS, Step Functions, and S3.
Develop AI-enabled features using LLMs, Agentic AI, and AWS services like Amazon Bedrock
and SageMaker.
Write clean, maintainable, secure, and testable code using Node.js or Python.
Implement event-driven workflows, asynchronous integrations, and serverless APIs.
Integrate AWS services programmatically using AWS SDK.
Cloud Engineering & Infra Automation:
Implement Infrastructure-as-Code (IaC) using CloudFormation.
Contribute to CI/CD pipelines using AWS Code Pipeline, Code Build, and Code Deploy.
Support observability by configuring logging, monitoring, tracing, and alerts.
Follow cloud security, compliance, and operational best practices.
AI/LLM Feature Implementation:
Build LLM-driven features, inference workflows, prompt-handling, and agent-based
automation using Bedrock.
Develop scalable AI components and integrate them into backend services.
Experiment with new AWS AI capabilities and contribute to PoCs.
Collaboration & Delivery:
Work with architects and senior engineers to translate high-level architecture into
implementation.
Participate in code reviews, design discussions, and team development practices.
Troubleshoot issues, optimize application performance, and support deployment
activities.
