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DevOps
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AI
ML
LLM
GPT
OpenAI
PoC
RAG
EC2
EKS

Summary

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.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: RTX1d9892
Position Id: Tej - 19
Posted 14 hours ago

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