We are looking for a hands-on Full Stack Engineer who builds modern applications with AI in the loop — using prompt engineering and spec-driven development to ship faster across multiple technology stacks. You will work across Java, .NET, Python, React, Angular and adjacent frameworks, refactor and modernize existing systems, contribute to solution design, and coach younger engineers along the way.
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Design, develop and deliver full stack applications end-to-end across backend (Java / .NET / Python / Node.js) and frontend (React / Angular) stacks.
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Use AI coding assistants and LLM-based tooling as a first-class part of the development workflow — from prototyping to production-grade code.
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Apply prompt engineering best practices and spec-driven development to translate business requirements into clear specs, then into AI-assisted implementations.
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Refactor and modernize legacy codebases — improve structure, testability, performance and maintainability without breaking behavior.
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Contribute to solutioning and architecture discussions: evaluate trade-offs, pick the right stack, and apply appropriate design patterns.
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Work flexibly across more than one project at a time, context-switching cleanly between domains and tech stacks.
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Coach and mentor junior engineers on coding standards, AI-assisted workflows, debugging, and design thinking.
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Participate in code reviews, technical estimations, and quality engineering practices (unit tests, CI/CD, observability).
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3–6 years of professional full stack development experience shipping production applications.
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Strong hands-on coding ability in at least two of: Java (Spring Boot), .NET (C# / ASP.NET Core), Python (Django / FastAPI / Flask), Node.js.
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Proficient with at least one modern frontend framework — React or Angular — including state management, component design and REST/GraphQL integration.
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Demonstrable experience using AI coding assistants (e.g., Claude, GitHub Copilot, Cursor, ChatGPT) to design, generate, refactor and review code in real projects.
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Working knowledge of prompt engineering best practices: context setting, role prompts, decomposition, examples, evaluation, and guarding against hallucination.
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Experience with spec-driven / specification-first development — turning requirements into clear specs, acceptance criteria and AI-executable instructions.
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Solid grasp of OOP, SOLID, common design patterns (factory, strategy, repository, adapter, etc.) and clean architecture principles.
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Proven track record of refactoring existing codebases — identifying smells, breaking down monoliths, improving test coverage and reducing tech debt.
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Comfort with relational and NoSQL databases, REST APIs, authentication/authorization, and basic cloud deployment (AWS / Azure / GCP).
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Version control (Git), CI/CD pipelines, and disciplined engineering practices.
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Strong communication skills — able to explain technical ideas to engineers and non-engineers alike.
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Experience building or integrating with LLM APIs, RAG pipelines, vector databases, or agentic frameworks.
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Familiarity with evaluation and testing practices for AI-generated code (golden tests, snapshot tests, eval harnesses).
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Exposure to micro-frontends, microservices, event-driven architectures or DDD.
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Containerization (Docker), orchestration (Kubernetes), and infrastructure-as-code.
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Open-source contributions or technical writing / public speaking.
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Comfortable juggling more than one project and switching context without dropping quality.
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Curious about new tools, especially in the AI / developer-tooling space, and quick to adopt what works.
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Generous with knowledge — you enjoy pairing with and coaching younger engineers.
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Bias for action, with a habit of breaking ambiguous problems into shippable increments.
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Opportunity to work on diverse projects across multiple stacks and domains.
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A culture that treats AI-assisted development as a core engineering competency, not a side experiment.
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Mentorship-driven team where coaching others is recognized and rewarded.
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Competitive compensation and growth path into senior / lead engineering roles.
Send your CV along with a short note describing one project where you used AI tools meaningfully in the development workflow — what you prompted, what you refactored, and what you learned.
