Senior Full Stack Developer

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.

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Role Scope

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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.