Front-End Engineering: Develop reusable UI components using React + TypeScript + Ant Design to create a consistent, maintainable user experience that accelerates feature delivery.
Backend Services: Build and maintain backend services using FastAPI (Python) to deliver reliable, secure, and scalable business capabilities.
API Build and Integration: Implement RESTful APIs following defined standards and guidelines, enabling interoperability and performance across services.
Data Engineering: Handle data transformation, aggregation, and integration tasks to ensure accurate, timely insights for analytics use cases.
Engineering Standards: Follow established system architecture and coding standards to ensure maintainability, compliance, and consistency with organizational frameworks.
Quality and Validation: Ensure code quality through testing, validation, and alignment with standards, growing confidence in releases and reducing defects.
Performance and Debugging: Support performance optimization and debugging across the application to improve user experience and system resilience.
DevOps and Delivery: Collaborate using Docker and continuous integration and delivery pipelines to ensure smooth deployment processes and faster, reliable releases.
Risk and Issue Management: Advance and communicate technical risks or blockers effectively to reduce delivery friction and protect timelines.
AI-Assisted Delivery: Use approved AI coding assistants such as GitHub Copilot, Claude Code, or equivalent tools to accelerate coding, refactoring, documentation, testing, debugging, and solution analysis while maintaining human oversight.
Coaching and Capability Building: Coach low-code/no-code developers into pro-code practices through upskilling, paired development, reusable templates, coding standards, and guided AI-assisted workflows.
Responsible AI Guardrails: Apply guardrails for AI-generated code, including human review, testing, security validation, documentation, and alignment with enterprise architecture standards.
Pro-Code Translation: Translate low-code/no-code prototypes into scalable pro-code solutions, balancing rapid prototyping with maintainability, extensibility, performance, and governance.
Patterns and Templates: Create reusable patterns, repository guidance, starter templates, and playbooks that help AI tools produce consistent, standards-aligned outputs.
Change Management: Support AI-assisted development change management through awareness, enablement, office hours, quick-start guides, feedback loops, adoption tracking, and reinforcement.
Cross-Functional Partnership: Partner with leads, BAs, solution teams, and developers to reduce SDLC effort through AI across requirements, design, build, test, deployment, and support.
Measurement and Reporting: Track adoption, productivity, quality outcomes, and risks from AI-assisted practices, and share progress, blockers, and lessons learned with stakeholders.