Full-Stack Engineer

4 days ago

Manila, National Capital Region, Philippines Remitly Full time ₱600,000 - ₱1,200,000 per year

We are seeking a results-driven engineer to design, develop, and maintain end-to-end web solutions that support AI and automation initiatives. This role requires expertise in modern front-end and back-end technologies, API integration, and cloud-based infrastructure.

Responsibilities:

  • Develop and maintain scalable front-end applications to support AI and automation projects.
  • Collaborate with data scientists, AI developers, and other engineers to integrate AI models and automation workflows into production systems.
  • Design and implement user-friendly interfaces using modern front-end technologies and frameworks.
  • Ensure the performance, quality, and responsiveness of front-end applications.
  • Develop and maintain back-end services and APIs to facilitate seamless data exchange between different components of the AI and automation ecosystem.
  • Troubleshoot and debug issues across the stack to ensure smooth operation of AI and automation solutions.
  • Stay updated with the latest industry trends and technologies to continuously improve our systems.

Must-Have Qualifications:

  • Bachelor's degree in computer science, Engineering, or a related field.
  • Proven experience as a Front-End, Back-End, or Full-Stack Engineer
  • Proficiency in front-end technologies such as HTML, CSS, JavaScript.
  • Strong knowledge of back-end technologies such as Python.
  • Experience with Django and Flask for web application development.
  • Experience with RESTful API design and implementation
  • Experience with database technologies like SQL, NoSQL, and ORM frameworks.
  • Understanding of version control systems, preferably Git.
  • Understanding of CI/CD pipelines and DevOps practices
  • Knowledge of cybersecurity best practices.

Nice-to-Have Qualifications:

  • ​Frameworks like React or Angular, experience is a plus.
  • Familiarity with containerization and orchestration tools like Docker and Kubernetes.
  • Experience with AI and machine learning frameworks (e.g., TensorFlow, PyTorch).
  • Knowledge of cloud platforms such as Azure, AWS, or Google Cloud
  • Experience with data visualization tools and libraries.


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