Technical Lead-

AI should be treated as part of the engineering workflow rather than simply another developer tool.

The Technical Lead will:

Drive adoption of AI-assisted software development across design, coding, debugging, testing, documentation and code review.

Be proficient with modern AI coding environments and assistants such as Claude Code, GitHub Copilot, Cursor or equivalent tools .

Demonstrate the ability to use AI effectively for understanding large codebases, generating and refactoring code, debugging complex problems and accelerating implementation.

Establish practices for safely using AI-generated code, including human review, automated testing, security validation and architectural consistency.

Use AI to accelerate creation of unit tests, integration tests, E2E tests, test data and regression coverage.

Understand concepts such as LLMs, RAG and AI agents sufficiently to collaborate with AI engineering teams and build AI-powered product experiences.

Understand how to integrate AI capabilities into web applications through APIs, streaming responses and tool-driven workflows.

Continuously experiment with emerging AI engineering capabilities while making pragmatic decisions about their production readiness.

8+ years of software engineering experience with significant experience designing and building large-scale frontend applications.

Strong hands-on expertise with React and modern React patterns .

Strong proficiency in TypeScript and modern JavaScript (ESNext) .

Deep understanding of Redux , Redux Saga and modern React-Redux patterns .

Strong understanding of React Hooks, component composition, rendering behavior, memoization and frontend performance.

Strong knowledge of HTML5, CSS, responsive design, browser APIs and modern web standards.

Experience designing reusable component libraries and design systems .

Strong understanding of frontend architecture patterns including modular architecture, domain-driven frontend design and micro-frontends where appropriate.

Experience designing frontend applications that consume REST APIs, GraphQL, WebSockets and event-driven APIs.

Understanding of authentication and authorization patterns including OAuth/OIDC, JWT and role-based access control.

Strong knowledge of browser performance, caching, network optimization and frontend security.

Experience building applications that handle large datasets, complex workflows, real-time updates and highly interactive user interfaces .

Understanding of JSON-driven UI engines and configuration-driven architectures, including dynamically rendering UI components, forms, workflows, and behaviors from configuration, is preferred.

Strong experience with several of the following:

React

TypeScript

Redux Saga

NPM / Yarn

Jest

React Testing Library

Playwright

Storybook

REST / GraphQL / WebSockets

Git and modern branching/development workflows

CI/CD using GitHub Actions, Jenkins or equivalent

Docker

AWS / Azure

Application monitoring and observability tools

Experience with Playwright or equivalent browser automation frameworks and the ability to drive a strong automated-testing strategy is particularly valuable.

The candidate should demonstrate the ability to reason beyond individual features and make architectural decisions involving:

Scalability and maintainability

Component and design-system architecture

Client state vs server state

API contracts

Performance and rendering strategy

Caching

Offline/PWA capabilities

Security

Observability

Feature flags

Internationalization

Accessibility

Backward compatibility

Incremental modernization of large existing applications

The candidate should be comfortable evaluating technologies based on business and architectural requirements rather than adopting frameworks simply because they are new.

Strong technical depth — able to go from architecture discussions to debugging code when necessary.

Architecture ownership — makes pragmatic technical decisions considering scalability, complexity, cost and delivery.

Engineering excellence — sets a high bar for code quality, testing, performance, security and maintainability.

AI-native mindset — actively looks for opportunities to use AI to improve engineering effectiveness rather than treating AI as an optional productivity tool.

Product thinking — understands the customer and business impact behind technical decisions.

Ownership — takes responsibility from problem definition through production delivery.

Continuous improvement — constantly challenges existing architecture, tooling and development practices.

Mentorship — raises the technical capability of engineers around them.

Clear communication — can explain complex architectural decisions to technical and non-technical stakeholders.

Global collaboration — comfortable working with geographically distributed engineering and product teams.

A successful Technical Lead / Architect at Zinier should be able to:

We are looking for someone who combines strong engineering fundamentals with curiosity about how modern frontend architecture and AI are changing the way enterprise software is built.