SUMMARY:
Role Purpose
The Senior Full Stack Developer is responsible for designing, building, integrating, testing, and supporting modern mobile and cloud-native digital solutions. The role requires strong hands-on engineering capability across native and cross-platform mobile applications, Java-based microservices, event-driven i...
POSITION INFO:
Role Purpose The Senior Full Stack Developer is responsible for designing, building, integrating, testing, and supporting modern mobile and cloud-native digital solutions. The role requires strong hands-on engineering capability across native and cross-platform mobile applications, Java-based microservices, event-driven integration, containerized platforms, cloud services, databases, and secure API delivery. The developer is expected to work as a senior engineering contributor within agile delivery squads, producing high-quality code, applying engineering discipline, and using agentic AI practices to improve delivery speed, code quality, testing coverage, documentation, and maintainability while maintaining strong human oversight and accountability. Â Role Scope Domain Expected Coverage Mobile apps Build and support native and cross-platform mobile applications using native iOS or Android patterns and React Native where appropriate. Backend services Design and develop Java-based microservices and APIs aligned to cloud-native, scalable, resilient, observable, and secure delivery principles. Integration Implement REST, SOAP, Kafka, AMQP, and enterprise integration patterns across internal systems, third-party platforms, and digital channels. Cloud platform Work with Azure, Docker, Kubernetes, YAML-based configuration, CI\/CD pipelines, cloud-native runtime patterns, and platform engineering standards. Data platforms Use MongoDB, Redis, relational stores where applicable, caching patterns, data access layers, and performance-aware persistence designs. Engineering model Deliver through agile squads, Git-based workflows, automated testing, DevOps practices, pair collaboration, and responsible use of agentic AI coding assistants. Â Â PREFERRED SKILLS AND KNOWLEDGE AREAS Experience delivering customer-facing digital banking, fintech, insurance, telecommunications, retail, or high-volume digital platforms. Strong understanding of cloud-native architecture, microservices, distributed systems, event-driven architecture, API-first delivery, and integration. Practical experience with Azure, Kubernetes, container security, configuration, deployment automation, and cloud operations. Working knowledge of mobile performance, API latency, caching, retries, circuit breakers, resilience, and high availability. Familiarity with OpenAPI, API contracts, JSON, XML, YAML, Swagger, Postman or equivalent API tools, and developer portals. Understanding of release management, feature toggles, blue-green or canary deployments, rollback, and production readiness. Exposure to secure software supply chain practices including dependency management, signed artefacts, SBOM, vulnerability scanning, and build pipeline controls. Ability to translate HLDs, LLDs, ADRs, integration designs, and non-functional requirements into working software. EXPERIENCE PROFILE Senior-level hands-on software development experience across mobile, backend, integration, and cloud-native environments. Strong Java microservices experience with API design, messaging, persistence, caching, deployment, and troubleshooting. Experience building or integrating mobile applications using native practices and React Native. Experience with Docker, Kubernetes, Azure, Git, CI\/CD, YAML configuration, cloud-native deployment, and production support. Experience with Kafka, AMQP, REST, SOAP, MongoDB, Redis, and enterprise integration patterns. Ability to use approved AI coding assistants responsibly for coding, testing, documentation, analysis, and delivery. CORE TECHNICAL REQUIREMENTS Mobile: Native mobile development, React Native, UI integration, device capabilities, performance, secure storage, app release support, and lifecycle management. Backend: Java, Spring Boot or equivalent frameworks, REST APIs, service decomposition, business logic, error handling, resilience, observability, and maintainability. Integration: REST, SOAP, Kafka, AMQP, event-driven architecture, synchronous\/asynchronous integration, message contracts, retries, idempotency, and troubleshooting. Cloud-native: Azure, Docker, Kubernetes, containers, YAML, configuration management, environment promotion, platform integration, and scalable deployments. Data: MongoDB, Redis, caching, data modelling, indexes, query performance, data access, and secure data handling. DevOps: Git, branching, pull requests, CI\/CD, build automation, artefact management, deployment automation, release support, and troubleshooting. Quality: Unit, integration, contract, component, and API testing, mobile test automation, code quality, and defect prevention. Security: Secure coding, OWASP, API security, secrets management, dependency scanning, input validation, token handling, encryption, and privacy. Observability: Logging, metrics, tracing, dashboards, alerts, correlation IDs, production support, diagnostics, performance analysis, and root-cause analysis. AI Engineering: Claude AI or equivalent approved coding assistants, prompt engineering, AI-assisted coding, testing, refactoring, review, and responsible AI governance. Key Responsibilities Design, develop, test, and maintain full-stack digital solutions across mobile, backend microservices, integration layers, and cloud-native platforms. Build mobile applications using native approaches and React Native, focusing on performance, usability, maintainability, and secure customer experiences. Develop Java microservices, APIs, backend components, integration adapters, event consumers\/producers, and business services. Implement RESTful APIs, SOAP integrations, asynchronous messaging, Kafka, AMQP, and enterprise integration workflows. Build cloud-native solutions using Docker, Kubernetes, Azure, container orchestration, configuration-as-code, and YAML deployment artefacts. Work with MongoDB, Redis, and other persistence or caching technologies to support scalable and resilient digital channels. Apply secure coding, API security, secrets management, dependency hygiene, and privacy-aware engineering practices. Write clean, maintainable, testable, and documented code aligned with engineering standards, architecture patterns, and team conventions. Develop automated unit, integration, contract, component, and end-to-end tests across mobile, services, and integration layers. Participate in code reviews, design walkthroughs, sprint planning, backlog refinement, defect triage, release planning, and technical problem solving. Troubleshoot production issues, analyze logs and telemetry, support root-cause analysis, and implement corrective actions. Collaborate with architects, product owners, business analysts, QA, DevOps, security, and platform teams to deliver quality outcomes. Contribute to API specifications, integration contracts, deployment notes, runbooks, and operational support guides. Mentor developers, share engineering practices, contribute reusable components, and improve development standards across squads. Agentic AI Engineering Responsibilities Use approved agentic AI coding assistants such as Claude AI or equivalent tools to accelerate delivery while retaining human accountability for code and design decisions. Use prompt-driven development to generate draft code, scaffolding, tests, documentation, refactoring plans, and implementation options. Use AI for codebase exploration, dependency analysis, defect investigation, log interpretation, and root-cause analysis. Use AI-assisted testing for unit, integration, contract, regression, mock data, and edge-case coverage. Use AI-assisted code reviews to identify defects, security concerns, performance risks, maintainability issues, and missing test coverage. Validate all AI-generated output through peer review, testing, security checks, static analysis, and engineering judgement before use. Do not expose confidential data, production secrets, customer information, credentials, proprietary code, or sensitive architecture to unapproved AI tools. Document relevant AI-assisted decisions, assumptions, test gaps, and verification steps where required by governance. Use AI without bypassing architecture governance, security controls, coding standards, quality gates, or regulatory requirements. Continuously improve prompts, reusable AI workflows, coding templates, and squad-level AI engineering practices.
