SUMMARY:

Role Purpose

The Junior Full Stack Developer supports the design, development, testing, integration, and maintenance of mobile and cloud-native digital solutions under the guidance of senior developers, technical leads, and architects. The role provides practical exposure across mobile applications, Java-based backend services, microser...

POSITION INFO:
Role Purpose The Junior Full Stack Developer supports the design, development, testing, integration, and maintenance of mobile and cloud-native digital solutions under the guidance of senior developers, technical leads, and architects. The role provides practical exposure across mobile applications, Java-based backend services, microservices, integrations, containerized platforms, cloud services, databases, and secure API delivery. The developer is expected to build strong engineering fundamentals, contribute to well-defined delivery tasks, follow established patterns and standards, and use approved agentic AI coding practices responsibly to improve learning, productivity, testing, documentation, and code quality with appropriate supervision and review. Guided agentic AI expectation The developer should use approved agentic AI tools, including Claude AI or equivalent enterprise-approved AI assistants, as a guided productivity and learning aid. AI usage should support code explanation, draft implementation, test generation, debugging, documentation, and refactoring suggestions, but all outputs must be reviewed, tested, and approved through normal engineering and peer-review processes.   Role Scope Domain Expected Coverage Mobile apps Support development of native and React Native mobile application features, bug fixes, UI integration, and basic app lifecycle tasks. Backend services Develop and maintain smaller Java service components, API endpoints, business logic, validation logic, and microservice features under guidance. Integration Assist with REST, SOAP, Kafka, AMQP, and enterprise integration tasks using existing patterns, contracts, and implementation examples. Cloud platform Work with Azure, Docker, Kubernetes, YAML, CI\/CD pipelines, and cloud-native deployment artefacts with support from senior engineers. Data platforms Use MongoDB, Redis, and related data or caching technologies for well-defined tasks, troubleshooting, and feature support. Engineering model Work within agile squads using Git, code reviews, automated testing, DevOps practices, documentation, and responsible AI-assisted development. Preferred Skills and Knowledge Areas Exposure to customer-facing digital products such as banking, fintech, insurance, telecommunications, retail, or high-volume digital platforms is advantageous. Understanding of microservices, API-first delivery, distributed systems, event-driven integration, and cloud-native practices. Awareness of Azure, Kubernetes, containerized deployment, configuration management, and environment promotion. Familiarity with OpenAPI, Swagger, Postman or equivalent API tools, JSON, XML, YAML, message schemas, and developer documentation. Basic understanding of release practices, feature flags, rollback, defect triage, production readiness, and operational support. Awareness of secure software supply chain practices including dependency management, vulnerability scanning, secrets management, and build pipeline checks. Willingness to learn from HLDs, LLDs, ADRs, integration designs, API contracts, and non-functional requirements. Experience Profile Entry-level to junior hands-on software development experience across mobile, backend, integration, or cloud-native environments. Foundational Java development experience and exposure to REST APIs, microservices, messaging, persistence, caching, or troubleshooting. Exposure to mobile development using native approaches, React Native, or related mobile frameworks is advantageous. Exposure to Docker, Kubernetes, Azure, Git, CI\/CD, YAML-based configuration, and cloud-native practices is advantageous. Awareness of Kafka, AMQP, REST, SOAP, MongoDB, Redis, and enterprise integration patterns. Ability to use approved AI coding assistants responsibly for learning, implementation, testing, documentation, and debugging. Core Technical Requirements Mobile: Foundational experience or strong learning ability in native mobile development, React Native, mobile UI integration, secure storage, app lifecycle, and mobile service integration. Backend: Core Java skills, exposure to Spring Boot or similar frameworks, REST APIs, service logic, validation, error handling, and maintainable backend development. Integration: Working knowledge of REST, SOAP, Kafka, AMQP, JSON, XML, message contracts, retries, basic idempotency, and integration troubleshooting. Cloud-native: Foundational understanding of Azure, Docker, Kubernetes, containers, YAML manifests, environment configuration, CI\/CD, and cloud-native deployment practices. Data: Basic hands-on exposure to MongoDB, Redis, data access patterns, caching, query basics, indexes, and secure data handling. DevOps: Git, branching, pull requests, build pipelines, deployment awareness, artefact basics, code review, and environment troubleshooting. Quality: Unit testing, API testing, integration testing, defect reproduction, test evidence, regression testing, and code quality discipline. Security: Secure coding fundamentals, OWASP awareness, input validation, secrets handling, dependency hygiene, token handling, and privacy-aware development. Observability: Basic logging, metrics, tracing, correlation IDs, dashboard usage, log investigation, and production support. AI engineering: Claude AI or equivalent approved coding assistants, prompt basics, AI-assisted testing, code explanation, documentation, and responsible AI usage. Key Responsibilities Develop, test, and maintain assigned features across mobile, backend, APIs, integrations, and cloud-native components. Support native and React Native development, including UI changes, service integration, defect fixes, enhancements, and basic performance improvements. Build Java service components, REST endpoints, integration adapters, message consumers\/producers, validation, and business logic under guidance. Assist with REST, SOAP, Kafka, AMQP, and enterprise integration patterns using documented contracts and architecture guidance. Use Docker, Kubernetes, Azure, YAML configuration, and CI\/CD pipelines for build, deployment, configuration, and troubleshooting tasks. Work with MongoDB and Redis for queries, data access, caching, investigation, and feature implementation. Write clean, maintainable code following team conventions, coding standards, security guidance, and architecture patterns. Create and maintain unit, integration, API, and regression tests for assigned work. Participate in code reviews, sprint ceremonies, backlog discussions, technical walkthroughs, defect triage, and release support. Investigate defects, analyze logs, reproduce issues, document findings, and support root-cause analysis with guidance. Contribute to technical documentation, API notes, implementation details, deployment notes, and support handover material. Seek feedback, learn from senior developers, and build capability across mobile, backend, integration, DevOps, cloud, data, and AI-assisted engineering. Guided Agentic AI Engineering Responsibilities Use approved agentic AI coding assistants such as Claude AI or equivalent tools for learning, code explanation, implementation, testing, debugging, and documentation. Use AI to draft code, scaffolding, tests, examples, and documentation, ensuring all outputs are reviewed, tested, and corrected before use. Use AI to understand existing code, compare implementation options, investigate defects, and identify areas for review. Use AI-assisted testing to draft unit tests, integration cases, mock data, edge cases, and regression checks. Validate AI-generated output through testing, static analysis, peer review, team standards, and senior developer guidance. Never share confidential information, production secrets, customer data, credentials, proprietary code, sensitive logs, or restricted architecture details with unapproved AI tools. Document relevant assumptions, limitations, and verification where AI materially influenced implementation or testing. Use AI as a learning and productivity aid, while maintaining engineering accountability and understanding. Ways of Working Work within agile squads using sprint planning, backlog refinement, daily collaboration, demos, retrospectives, and continuous improvement. Use Git-based workflows with clear commits, pull requests, peer reviews, and traceability to assigned work. Collaborate with developers, DevOps, QA, architects, designers, product owners, and business analysts. Ask for guidance early, document assumptions, provide test evidence, and escalate blockers or risks promptly. Use agentic AI responsibly while maintaining ownership, understanding, testing discipline, and engineering standards.