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.