AI Native Engineer

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.