Full Stack Engineer – Agentic AI (AWS)

Role Overview
We are looking for experienced Full Stack Engineers with strong hands-on expertise in AWS cloud technologies and the ability to design, develop, and deliver Agentic AI use cases. The ideal candidate should have experience building scalable full-stack applications, integrating Generative AI solutions, and leveraging AWS services such as Amazon Bedrock, Kiro, and Amazon QuickSight to develop intelligent, business-driven applications.
Key Responsibilities
Design, develop, and maintain scalable full-stack applications using modern frontend and backend technologies.
Build and implement Agentic AI solutions capable of executing multi-step tasks, reasoning, and workflow automation.
Develop AI agents and Generative AI applications using Amazon Bedrock, including foundation models, tool calling, and agent orchestration.
Leverage Kiro for AI-assisted software development and application delivery.
Integrate AI-powered capabilities into existing enterprise applications, APIs, and business workflows.
Develop interactive dashboards, reports, and business intelligence solutions using Amazon QuickSight.
Design and implement secure, scalable cloud-native applications using AWS services.
Build and integrate REST APIs, microservices, databases, and third-party systems.
Implement authentication, authorization, logging, monitoring, and security best practices.
Collaborate with product owners, architects, and cross-functional teams to translate business requirements into technical solutions.
Optimize application performance, reliability, scalability, and cloud costs.
Follow CI/CD, version control, testing, and deployment best practices.
Required Skills
Full Stack Development: 5+ years of experience in application development using frontend and backend technologies.
AWS Cloud: Hands-on experience with AWS services, architecture, deployment, and application integration.
Agentic AI: Experience developing AI agents, autonomous workflows, tool calling, multi-step orchestration, and enterprise AI use cases.
Amazon Bedrock: Experience with foundation models, prompt engineering, knowledge bases, RAG, and agent-based applications.
Kiro: Familiarity with AI-powered development workflows using Kiro.
Amazon QuickSight: Experience creating dashboards, visualizations, reports, and business insights.
Backend Development: Experience with Python, Node.js, Java, or similar backend technologies.
Frontend Development: Experience with React, Angular, or similar modern frontend frameworks.
API & Integration: Strong knowledge of REST APIs, microservices, and system integration.
Database: Experience with SQL/NoSQL databases and data modeling.
DevOps: Familiarity with Git, CI/CD pipelines, automated testing, and cloud deployment.
AWS Certification
Candidates should hold a relevant AWS certification, preferably one of the following:
AWS Certified Developer – Associate
AWS Certified Solutions Architect – Associate
AWS Certified Machine Learning Engineer – Associate
AWS Certified AI Practitioner
Preferred Qualifications
Experience delivering production-grade Generative AI or Agentic AI applications.
Knowledge of RAG architectures, vector databases, embeddings, and prompt optimization.
Experience with AWS Lambda, API Gateway, Amazon S3, IAM, CloudWatch, and other AWS services.
Understanding of AI security, responsible AI practices, monitoring, and evaluation.
Strong problem-solving, communication, and collaboration skills.
Skills: kiro,bedrock,aws

Work arrangement
Hybrid