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.