Full Stack Engineer
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Key skills for this role
About the Role
Vistas Global is seeking an AI Native Engineer to design, build, and operate solutions end-to-end using an agentic IDE. The role requires full-stack engineering experience with modern React/Next.
Key Skills for This Role
Responsibilities
- Design, build, and operate solutions end to end as a full stack engineer using an agentic IDE as the primary development surface
Requirements
- Full stack engineering experience shipping production features end to end — UI (modern React / Next.js), API (typed RPC or REST), database schema and queries, and at least one async / workflow surface
- Vibe Coding experience — non negotiable. Daily, production use of an agentic IDE as the primary development surface
- TypeScript fluency; comfortable writing database migrations and reasoning about relational query plans, indexes, and lock behavior
- AI agent validation and AI quality control: familiarity with testing LLM based and agentic systems
- Understanding of how AI first applications are built (LLMs, RAG, agents) sufficient to design meaningful tests for them
Full Job Posting
Objective
- Design, build, and operate Solutions end to end as a full stack engineer using an agentic IDE as the primary development surface (Vibe Coding).
Core Technical Skills
- Full stack engineering experience shipping production features end to end — UI (modern React / Next.js), API (typed RPC or REST), database schema and queries, and at least one async / workflow surface.
- Vibe Coding experience — non negotiable. Daily, production use of an agentic IDE as the primary development surface.
- TypeScript fluency; comfortable writing database migrations and reasoning about relational query plans, indexes, and lock behavior.
AI & Vibe Logic Competencies
- AI agent validation and AI quality control (must have): familiarity with testing LLM based and agentic systems — prompt/response validation, hallucination and bias detection, guardrail and safety testing, tool use and workflow validation, and the use of evaluation datasets, ground truth comparison,
- Understanding of how AI first applications are built (LLMs, RAG, agents) sufficient to design meaningful tests for them.
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