profile.
He/she needs to work in hybrid model, at least 2 days from
Whippany NJ
office.
Job Description:
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.
Core Responsibilities
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, GitHub 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).
Required Technical Skills
Programming Languages:
High proficiency in Python and TypeScript/JavaScript (React, Next.js, Node.js).
AI Frameworks & Libraries:
Experience with LangChain, LangGraph, LlamaIndex, 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 GitHub Copilot.
Software Engineering Fundamentals:
Strong understanding of Git, debugging, testing, API design, and clean code principles.
Preferred Qualifications
Experience building custom GPTs, Claude Projects, or Multi-agent orchestration.
Understanding of AI governance, security, and human-in-the-loop mechanisms.
Experience with DevOps and MLOps tools (MLFlow, Kubeflow).
Key Characteristics
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.