Full Stack Engineer

Position Summary

We are seeking a Full Stack Software Engineer with strong experience in cloud-native development, API design, and DevOps practices. The ideal candidate will also have experience building AI-enabled applications, including LLM integrations, Retrieval Augmented Generation (RAG) architectures, and vector-based knowledge systems.

This role will contribute to the development of next-generation intelligent applications by integrating modern AI services, cloud platforms, and scalable backend systems while leveraging AI-assisted development tools and cloud-native architectures.

Key Responsibilities

Full Stack Application Development

• Design and develop scalable full-stack applications using modern frontend frameworks and backend technologies.
• Develop responsive UI components and integrate them with backend services and APIs.
• Implement secure, maintainable, and high-performance application code.

API & Cloud-Native Development

• Develop RESTful or GraphQL APIs to support application functionality and external integrations.
• Build and deploy applications on AWS and Azure cloud platforms.
• Design microservices-based architectures to support scalable enterprise applications.

AI-Enabled Application Development

• Develop and integrate AI-driven capabilities into applications using modern AI frameworks and cloud services.
• Integrate large language models (LLMs) to enable conversational interfaces, intelligent automation, and knowledge discovery features.
• Design and implement Retrieval Augmented Generation (RAG) architectures to enhance AI responses with enterprise knowledge sources.

Agentic AI & Intelligent Workflows

• Design and develop agent-based AI workflows that can autonomously perform tasks, orchestrate services, and support decision-making processes.
• Implement AI agents capable of interacting with APIs, databases, and enterprise systems.
• Support development of multi-step reasoning workflows and automation powered by LLMs.

Vector Databases & Knowledge Retrieval

• Implement vector-based search and semantic retrieval systems for AI-enabled applications.
• Work with vector databases such as Pinecone, Weaviate, OpenSearch vector search, or similar platforms.
• Design pipelines for embedding generation, indexing, and semantic search.

AI-Assisted Development Tools

• Utilize and evaluate AI-powered developer productivity tools, including:
• GitHub Copilot
• Claude Code
• Cline
• Amazon Q Developer
• AWS CodeWhisperer
• Contribute to evaluation and adoption of modern AI development tooling and engineering productivity platforms.

DevOps & CI/CD

• Implement and maintain CI/CD pipelines to automate build, testing, and deployment.
• Work with DevOps teams to ensure reliable deployments, monitoring, and performance optimization.
• Support infrastructure automation using cloud-native DevOps tools.

Technology Evaluation & Innovation

• Participate in technical evaluations, proofs-of-concept, and technology assessments for emerging tools and platforms.
• Contribute to engineering standards, architecture guidelines, and development best practices.
• Stay current with evolving AI, cloud, and developer productivity technologies.

Required Qualifications

• Bachelor’s degree in computer science, Engineering, or related field.
• 7+ years of experience in full-stack software development.
• Experience with modern frontend frameworks (React, Angular, or Vue.js).
• Backend development experience with Node.js, Java, Python, or similar technologies.
• Experience designing and developing REST APIs and microservices.
• Experience with AI-assisted and agentic AI development tools, such as GitHub Copilot, Amazon CodeWhisperer, Cline, Claude Code, or similar solutions.
• Hands-on experience with AWS and/or Azure cloud platforms.
• Experience with CI/CD pipelines and DevOps practices.

Preferred Qualifications

• Experience integrating LLM-based applications.
• Experience designing RAG-based architectures.
• Familiarity with vector databases and semantic search technologies.
• Experience working with AWS AI/ML services such as SageMaker, Bedrock, or Amazon Q Developer.

Knowledge of container technologies (Docker, Kubernetes).