AI-Native Software Engineer (Agentic Development & AI Engineering) Agent-driven development: Builds production software primarily through AI coding agents (e.g., Claude Code, Cursor, Copilot), with the context, tools, and guardrails that keep them reliable. Spec-driven development: Turns requirements and user stories into precise specs and acceptance criteria that agents can execute against. LLM and prompt engineering: Works fluently with LLM APIs, designs effective prompts and context, and understands their limits, such as hallucination, context windows, and cost. Agentic workflows: Designs and debugs multi-step agent workflows with tool calling, parallel work streams, and human checkpoints. AI output verification: Tests, debugs, and validates AI-generated code and outputs through automated tests, code review, and runtime checks. Positive to Have AI application development:



Has built and shipped AI-powered features, such as chat, retrieval, or document processing, to real users. RAG and vector search: Experience building retrieval over HR and people-related content, such as policies, process documentation, and structured HR records, using embeddings and a vector store. pgvector on PostgreSQL is preferred, with Pinecone or Chroma as alternatives. AI frameworks and protocols: Experience with the TypeScript AI stack (LangChain.js, LangGraph.js), LlamaIndex and MCP for connecting agents to tools such as Jira, SharePoint, and our HCM services. Deployment and operations: Ships AI systems with Docker, CI/CD, cloud platforms, and monitoring of model quality, latency, and cost.

📌 AI Native Software Engineer (Hyderabad)
🏢 QVANTEL
📍 Hyderabad