Software Engineer

Solution Design & Ownership: Independently design and implement scalable, production-ready applications; front-end, back-end APIs, and data storage, etc…

Technology Stack: Apply expert-level knowledge across modern JavaScript frameworks (React, Vue, Angular), microservices, cloud platforms (like GCP, AWS, OCI, or Azure), and performance-optimised databases.

Quality & DevOps Champion: Establish best practices for clean code, comprehensive testing (unit, integration, E2E), security, and CI/CD, driving an always-shippable product.

Customer-Centric Solutioning: Translate complex business requirements into simple, intuitive software experiences that clearly solve the problem.

Strategic Reusability: Architect with modularity and componentisation in mind, building frameworks and patterns that can be rapidly tailored for new clients across sectors.

Advanced Prototyping: Lead research and prototyping of complex features; real-time data integration, highly interactive UIs.

AI-Accelerated Engineering: Use AI coding tools (e.g., Claude Code) to move faster; generating boilerplate, debugging, writing tests, and documentation while staying deliberate about where human review and judgment must take over.

Cross-Functional Partnership: Collaborate closely with product, UX/UI, and business stakeholders to ensure successful technical definition and delivery of end-to-end solutions.

Knowledge Sharing: Document technical designs, share best practices, and raise the engineering quality bar across the engineering team.

Full-Stack Acumen: Expert-level proficiency in a modern front-end framework (React/ReactNative) and a back-end environment (Node.js/Express, Java Spring Boot, Python, etc.).

Cloud & DevOps Mastery: Significant hands-on experience architecting and deploying on a major cloud platform (like GCP, AWS, OCI, or Azure), with proven CI/CD, monitoring, and containerisation (Docker) expertise.

Data & Infrastructure: Hands-on expertise with relational and/or NoSQL databases; familiarity with serverless functions, application gateways, and networking. Infrastructure-as-Code (IaC); for example Terraform, experience is a plus.

End-to-End Delivery: A track record of taking applications from concept through to live production, with an unwavering focus on reliability and performance.

Ownership & Ambiguity: Proven ability to take highly ambiguous business problems and translate them into well-defined, delivered technical solutions.

Communication: Exceptional ability to explain complex architectural decisions to both technical and non-technical audiences.

Team Player: Transparent and communicative, anticipating roadblocks and prioritising the team's success over individual credit.

Continuous Learning: A strong bias for fast, self-directed learning and staying current with new frameworks and best practices.

Data Engineering Skills: Experience with data pipelines, ETL/ELT processes, or working with large-scale/streaming data.

AI Tooling & Workflows: Practical experience building AI-powered features (e.g., using the Claude API or similar), not just using AI as a coding assistant. Including prompt design, tool use, function calling, and evaluating model outputs in production.

Experience across multiple cloud platforms (GCP, AWS, Azure, OCI).

Experience contributing to reusable internal frameworks or libraries deployed across multiple clients.