AI-Native Developer

AI-Native Developer

An AI-Native Developer (or AI-Native Engineer) experienced to build applications with Artificial Intelligence embedded into their core architecture, workflows, and delivery lifecycle from day one, rather than treating AI as a tacked-on feature. Focus mainly on model training, AI-native developers specialize in using AI to write code, leveraging LLMs (Large Language Models), and constructing agentic workflows to accelerate production.

• Agentic & LLM System Development:
  Build autonomous or semi-autonomous agents, orchestrate agent planning loops, manage tool calling, and implement memory modules.
• AI-Powered Coding:
  Use AI tools (e.g., Cursor, Git Hub Copilot, Claude Code) to rapidly prototype and generate production-ready code.
• RAG Pipeline Construction:
  Develop Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search.
• API/SDK Integration:
  Integrate LLMs (OpenAI, Anthropic) into applications using function calling, structured outputs, and workflow automation.
• Production Deployment:
  Take AI prototypes from Proof of Concept (PoC) to deployment using cloud platforms (AWS, GCP, Azure, Vercel).

• AI Frameworks & Libraries:
  Experience with Lang Chain, Lang Graph, Llama Index, or Semantic Kernel.
• Vector Databases:
  Familiarity with technologies such as Pinecone, Chroma, Milvus, or Vertex AI Vector Search.
• Development Tools:
  Hands-on experience with AI coding tools such as Cursor, Claude Code, and Git Hub Copilot.
• Software Engineering Fundamentals:
  Strong understanding of Git, debugging, testing, API design, and clean code principles.

• Experience building custom GPTs, Claude Projects, or Multi-agent orchestration.
• Understanding of AI governance, security, and "human-in-the-loop" mechanisms.
• Experience with Dev Ops and MLOps tools (MLFlow, Kubeflow).

• AI-Centric Mindset: Solves problems by blending human judgment with machine intelligence, producing 3–10× more output.
• Adaptability: Learns new AI tools faster than the industry can create them.
• Product Focus: Focuses on building, optimizing, and deploying AI applications quickly rather than just researching models.