What are the responsibilities and job description for the AI Native Engineer position at VRN Technologies?
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.
Salary : $50 - $70