AWS + AI-Native Developer

AWS AI-Native Developer

Whippany NJ - Hybrid Role

Contract

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, 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.