AI Native Engineer

VRN Technologies; Jersey City (NJ); Hybrid; USD 120,000 - 180,000; Full time

Job summary

VRN Technologies seeks an AWS + AI-Native Developer to embed AI within core architecture from day one. You will focus on model training and AI-driven code, leveraging LLMs to accelerate production.

You will build agentic workflows, integrate LLMs, and deploy AI prototypes to production using AWS, GCP, and Azure, with emphasis on writing robust, maintainable production code.

Qualifications

• Strong proficiency in Python and TypeScript/JavaScript (React, Next.js, Node.js).
• Experience with AI frameworks and vector databases for AI-native apps.
• Familiarity with LLM tooling and AI-driven coding workflows.

Responsibilities

• Work on AWS services (EC2, EKS, DynamoDB, Lambda, API Gateway, S3) and AWS Bedrock.
• Develop agentic workflows and memory modules for AI-native systems.
• Use AI tools (Cursor, GitHub Copilot, Claude Code) to generate production-ready code.
• Build RAG pipelines using vector databases and semantic search.
• Integrate LLMs (OpenAI, Anthropic) via function calling and APIs.
• Lead production deployment across cloud platforms (AWS, GCP, Azure).

Skills

Python

TypeScript/JavaScript

React

Next.js

Node.js

LangChain

LangGraph

LlamaIndex

Semantic Kernel

Pinecone

Chroma

Milvus

Vertex AI Vector Search

Cursor

Claude Code

GitHub Copilot

Git

Debugging

Testing

API Design

Clean Code

Tools

Python

TypeScript/JavaScript

React

Next.js

Node.js

LangChain

LangGraph

LlamaIndex

Semantic Kernel

Pinecone

Chroma

Milvus

Vertex AI Vector Search

Cursor

Claude Code

GitHub Copilot

Git

Debugging

Testing

API Design

Clean Code

Job description

Job Description
An AWS + AI-Native Developer (or AI-Native Engineer) experienced to build applications with Artificial Intelligence embedded using AWS Bedrock, 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.

Position: AWS + AI-Native Developer
Location: Whippany, NJ (Hybrid)
Duration: Long-Term
Job Description
An AWS + AI-Native Developer (or AI-Native Engineer) experienced to build applications with Artificial Intelligence embedded using AWS Bedrock, 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

• AWS - Hands on with core services (EC2, EKS, DynamoDB, Lambda, API Gateway, S3)
• AWS Bedrock
• 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, Google Cloud Platform, 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.