Full Stack Engineer - AI Products

• Develop and maintain web applications with a focus on AI-based predictions and recommendations.
  
• Design and implement scalable, modular, and reusable solutions using microservices architecture with Kubernetes and Docker.
  
• Build and optimize frontend and backend components using technologies such as Django/FastAPI + React, Ruby on Rails, or Flask + Vue.js.
  
• Structure projects effectively, applying best development and testing practices to ensure high-quality code.
  
• Build and maintain CI/CD pipelines to automate parts of the development cycle, ensuring efficient and reliable deliveries.
  
• Leverage cloud services (preferably Azure) to deploy and manage applications.
  
• Contribute to the development of agent architectures/pipelines and MLOps pipelines to automate the lifecycle of AI models.
  
• Utilize AI copilots to accelerate development processes where applicable.
  
• Collaborate with cross-functional teams, including data scientists and product managers, to deliver AI-driven solutions.
  
• Troubleshoot and resolve technical issues to ensure robust and scalable product performance.
  

Requirements

• Minimum of 3 years of experience in web application development.
  
• Proficiency in frontend and backend development technologies (e.g., Django/FastAPI + React, Ruby on Rails, Flask + Vue.js).
  
• Ability to design scalable, modular solutions based on microservices architecture using Kubernetes and Docker.
  
• Strong project structuring skills, with expertise in applying best development and testing practices.
  
• Experience with CI/CD pipelines, including building and maintaining pipelines to automate development processes.
  
• Familiarity with cloud services, preferably Azure.
  
• Strong problem-solving skills and ability to work collaboratively in a fast-paced environment.
  
• Direct experience with AI-based prediction/recommendation tools is a plus.
  
• Experience in developing agent architectures/pipelines is valued.
  
• Experience in productizing MLOps pipelines for AI model lifecycles is valued.
  
• Familiarity with AI copilots to accelerate development is valued.
  
• Exposure to distributed computing technologies (e.g., Hadoop, Spark) is valued.
  

• Certifications in relevant technologies (e.g., Microsoft Certified: Azure Developer Associate, Certified Kubernetes Application Developer).
  
• Experience with MLOps frameworks (e.g., MLflow, Kubeflow).
  
• Familiarity with observability tools (e.g., Prometheus, Grafana) for monitoring AI-driven applications.
  
• Knowledge of Agile methodologies and DevOps practices.