Build REST and gRPC endpoints following agreed contracts, implementing authentication and authorisation integration, input validation, error handling, and secure data handling practices
Implement SQL-backed business logic powering APIs and micro-services, including joins, aggregations, filtering, pagination, and transactional workflows
Contribute to CI/CD pipelines and Git workflows, packaging and deploying services using containers and Kubernetes with support for safe rollouts and rollbacks
- Contribute to infrastructure-as-code using Terraform within established team patterns across modules, environments, and state management
- Improve operability of services by adding and using observability tooling including logs, metrics, traces, dashboards, and alerts, and participate in incident response and root-cause analysis
Leverage enterprise-authorised AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards
Apply knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorised AI-assisted development and automation capabilities, to improve the value realised by automation
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and proficient applied experience
Strong hands-on JavaScript, Java or Python development experience building backend services, including testing, packaging, dependency management, and maintainability
Strong database and SQL proficiency, with experience implementing application logic and APIs on top of relational data
Experience building APIs and micro-services using REST or gRPC, including contracts, security basics, and observability
Practical experience delivering LLM-based features as part of software systems, with familiarity with agentic patterns
Working knowledge of delivery and operations including CI/CD, and Git
Familiarity with Kubernetes and cloud infrastructure concepts in a multi-cloud environment
Solid understanding of software engineering fundamentals and software development lifecycle practices including design, reviews, testing, release, and production support
Hands-on experience using enterprise-authorised AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate AI-generated outputs
Strong experience with container orchestration platforms (Kubernetes distributions such as EKS, AKS, GKE or OpenShift, or managed equivalents such as ECS or Azure Container Apps), and with the cloud infrastructure that supports them.
Experience with relational and/or NoSQL databases in the cloud (e.g. RDS/Aurora, DynamoDB, Azure SQL/Cosmos DB, GCP Cloud SQL/Firestore, Oracle).
Preferred skills
Familiarity with Terraform and cloud infrastructure concepts in a multi-cloud environment
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations
Strong debugging and troubleshooting skills in distributed systems, including root-cause analysis and performance bottleneck identification using logs, metrics, and traces
Experience improving code quality and reliability through test strategy improvements, refactoring, static analysis, and dependency hygiene
Experience with deployment and operational best practices including safe releases, rollbacks, environment configuration, and incident readiness
Familiarity with common architecture patterns such as event-driven designs, async processing, caching, API versioning, and backward compatibility
Experience collaborating effectively in agile delivery, including estimating, breaking down work, documenting decisions, and communicating risks
