Senior AI Developer - Python, GenAI & API Microservices

Robotic Technologies Automation; Berkeley Heights (NJ); On-site; USD 89,000 - 126,000; Full time

Job summary

Robotic Technologies Automation in Berkeley Heights, NJ, seeks an AI Developer with Python for an 11‑month engagement with extension options. The role offers a bill rate of $78/hour on a W-2/1099/C2C basis and is based in Berkeley Heights, NJ.

You will design and build AI‑driven backend systems, implement Retrieval‑Augmented Generation (RAG), integrate LLMs with vector stores and external REST APIs, and deploy production‑ready code using Docker and cloud platforms (AWS, Azure, GCP).

Qualifications

• 8+ years of total software experience with deep Python proficiency.
• Experience with GenAI, NLP, prompt engineering, and vector stores.
• Knowledge of web frameworks such as FastAPI, Flask, or Django.
• Familiarity with MLOps practices, Git version control, and containerization.

Responsibilities

• Design and build AI-driven applications, data pipelines, and microservices using Python.
• Implement Retrieval-Augmented Generation (RAG) and orchestrate AI agents using tools like LangChain, LangGraph, or CrewAI.
• Integrate Large Language Models (LLMs) with enterprise vector databases and external REST APIs.
• Optimize backend performance and deploy production-ready code via Docker and cloud platforms (AWS, Azure, GCP).

Skills

Python

GenAI

NLP

Prompt engineering

Vector stores

Web frameworks

MLOps

Git

Containerization

Tools

LangChain

LangGraph

CrewAI

Docker

AWS

Azure

GCP

Vector databases

Job description

Robotic Technologies Automation in Berkeley Heights, NJ, seeks an AI Developer with Python for an 11‑month engagement with extension options. The role offers a bill rate of $78/hour on a W-2/1099/C2C basis and is based in Berkeley Heights, NJ.

You will design and build AI‑driven backend systems, implement Retrieval‑Augmented Generation (RAG), integrate LLMs with vector stores and external REST APIs, and deploy production‑ready code using Docker and cloud platforms (AWS, Azure, GCP).