Full Stack Developer -Product Engineer

Key Responsibilities

• Product Ownership: Drive end-to-end product development, taking features from concept to production with minimal supervision.
• Iterative Development: Build, ship, and iterate rapidly to meet business deadlines and user requirements.
• Cross-functional Collaboration: Collaborate closely with product managers and designers to translate user needs into technical specifications and acceptance criteria.
• Software Craftsmanship: Write high-quality, scalable code and ensure the performance and maintainability of the codebase.
• Technical Problem Solving: Analyse complex systems and resolve technical issues from development to production.
• AI-Augmented Software Development: Use AI coding assistants and agents to accelerate development of product features. Generate, review, and refine code produced by AI tools.
• Validation: Take full responsibility of unit testing and ensure bug free code is delivered. Also, validate correctness, security, and performance of AI-generated code.

Product Component Development

• Design and implement: Microservices, APIs and integration services, Data pipelines, Event-driven architectures, Domain services
• Ensure all components are: Scalable, Observable, Secure, Cloud-ready and are maintainable for long-term.
• AI Agent Orchestration: Use modern AI frameworks to automate engineering workflows such as: Code generation, Test creation, Documentation, Refactoring, Debugging
• Quality Engineering: Ensure high product quality by reviewing AI-generated code, writing automated tests, performing performance and security reviews
• Continuous Delivery and DevOps: Work with modern DevOps practices which includes adhering to defined CI/CD pipelines and building all components for cloud-native deployment.

Required Skills

• Strong experience in at least one major programming language such as: Java, Python, Go, TypeScript
• Experience withMicroservices architecture, REST / GraphQL APIs, Distributed systems, Databases (SQL and NoSQL)
• Experience using AI engineering tools such as GitHub Copilot, Cursor, Claude, etc.
• Ability to evaluate AI-generated code, integrate AI tools into development workflows
• Experience with cloud platforms such as Amazon Web Services, Microsoft Azure, or Google Cloud Platform with focus on containers and orchestration, infrastructure automation and observability tools
• Experience level: 5 to 8 years