Software Engineer (Full Stack)
Job Summary
We are seeking a versatile Full Stack Developer to join our product development team. This role is responsible for the end-to-end lifecycle of web applications, from designing intuitive user interfaces (UI) to managing robust server-side architecture and databases. The ideal candidate is a proactive problem-solver who can navigate all layers of the technical stack to deliver high-quality, scalable solutions.
Key Responsibilities
Frontend Development: Design and implement responsive, visually appealing user interfaces using HTML5, CSS3, and JavaScript. Develop single-page applications (SPAs) leveraging frameworks like React.js, Angular, or Vue.js.
Backend Development: Build and maintain scalable server-side logic and APIs using Node.js, Python (Django/FastAPI), or Java (Spring Boot).
Database Management: Design, optimize, and manage SQL (MySQL, PostgreSQL) and NoSQL (MongoDB) database schemas and queries.
Deployment & DevOps: Manage the complete software development process from conception to deployment. This includes containerization with Docker, setting up CI/CD pipelines (e.g., GitHub Actions), and monitoring application performance.
Collaboration: Work closely with product managers, graphic designers, and other engineers in an Agile environment to translate requirements into functional code.
Quality Assurance: Implement unit, integration, and end-to-end testing to ensure software reliability and security.
Qualifications & Skills
Education: Bachelor’s degree in Computer Science, Engineering, or a related field.
Experience: Typically 3–5 years of professional experience in full stack roles.
Core Technical Stack:
Languages: JavaScript (ES6+), Python, or PHP.
Frameworks: React.js, Node.js/Express, and Java/Spring Boot.
Version Control: Experience with Git and Bitbucket.
Cloud Services: Basic to advanced knowledge of AWS (EC2, S3, Lambda) or Azure.
Soft Skills: Strong analytical thinking, excellent communication, and the ability to mentor junior developers.
Additional Preferred Experience
Familiarity with AI/ML integration, such as using LLMs or agentic frameworks (e.g., LangChain).
Familiarity with AI Coding Assistants, such as GitHub Copilot, Claude Code, AmazonQ Developer, etc.
Knowledge of specialized visualization libraries like D3.js.