Full Stack AI Engineer

About the Role :

GrowthArc is looking for a React + AI Engineer who combines strong frontend engineering with hands-on experience building AI and LLM-powered systems in production.

You will work on building intelligent web applications, agentic workflows, and dynamic React interfaces that solve real business problems at scale.

What You Will Work On :

• Build and scale complex React applications with multi-step forms, dynamic page builders, and document management.

• Design AI-assisted workflows that reduce manual intervention using agents and LLMs.

• Develop agentic systems with MCP tooling, multi-agent orchestration, and RAG pipelines.

• Integrate LLMs into frontend and backend workflows including prompt engineering, tool routing, and structured outputs.

• Build observability into AI systems including logging, tracing, evals, and guardrails.

Must-Have Skills :1. React & Frontend :

• Deep knowledge of React hooks such as useMemo, useCallback, useContext, and useReducer.

• Complex form management with react-hook-form and schema validation using Zod or Yup.

• State management using Context, Redux, or Zustand with an understanding of when to use which.

• Performance optimisation including code splitting, memoization, and virtualized lists.

• Component architecture including atomic design, design systems, and reusable libraries.

• Strong system design with the ability to architect large-scale React applications from scratch.

2. AI / Agentic Systems :

• Clear understanding of Agentic systems including planning, tool use, looping, and agent handoffs.

• Hands-on experience with MCP tools and agent-to-tool routing logic.

• Multi-agent orchestration including supervisor/worker, sequential, and parallel patterns.

• Prompt engineering including system roles, few-shot, chain-of-thought, and structured outputs.

• RAG implementation including Hybrid RAG (vector + BM25) at minimum, with Knowledge Graph RAG as a plus.

• AI observability including tracing LLM calls, evals like RAGAS or DeepEval, and guardrails.

3. Backend & System Design :

• API design including RESTful contracts, gateway patterns, and auth flows.

• Backend design for document ingestion, approval routing, and status tracking.

• Observability including structured logs, metrics, and distributed tracing.

4. AI Frameworks & Tools :

• LangChain, LlamaIndex, or similar LLM orchestration frameworks.

• Vector databases.

• Knowledge graph tooling such as Neo4j or similar.