AI/ML Engineers (Python, Numpy, Cloud, Dialog Flow/Playbook, AI/ML And Kubernetes) - Must work on W2 basis only

Overview

On Site
Depends on Experience
Contract - W2
Contract - 12 Month(s)

Skills

Machine Learning (ML)
Generative Artificial Intelligence (AI)
Machine Learning Operations (ML Ops)
NumPy
LangChain
Kubernetes
Python
Cloud
Dialog Flow/Playbook
Ai/Ml And Kubernetes

Job Details

AI/ML Engineers (Python development, NumPy, Kubernetes, GenAI orchestration tools (LangGraph, LangChain, ReACT, LLM), Conversational AI, Google Cloud Platform (Google Cloud) or similar, MLOps practices, ML frameworks, ResponsibleAI (RAI), Google Dialog Flow and Playbook)

Iselin, NJ, Charlotte, NC or Chandler, AZ (3 days onsite in a week)

12+ months contract

Must work on W2 basis only

  • Must have a strength in Python development with specific experience in NumPY ideally
  • Must have one of the following: Google Dialog Flow; Playbook
  • Must be familiar with ADK
  • Must have cloud experience, ideally in Google Cloud Platform but Azure or AWS are acceptable will easily pick up Google Cloud Platform
  • Must be able to work within Kubernetes or similar
  • Proven experience in Conversational AI and synthetic agent development, especially within Google Cloud environments (or any flavor of cloud)
  • Hands-on expertise with GenAI orchestration tools (LangGraph, LangChain, ReACT, LLMs, etc.)
  • Strong background/exposure to real-time, event-driven architectures and cloud-native technologies (Google Cloud Platform, Kafka, Pub/Sub, Big Table).
  • Deep understanding of MLOps practices for scalable AI deployment and monitoring.
  • Experience in Responsible AI (RAI) within highly regulated industries.
  • Build smart AI agents using Google's Conversational Platform and playbooks to improve automated interactions.
  • Use tools like LangGraph, LangChain, and large language models (LLMs) to manage multiple AI agents working together.
  • Apply machine learning and MLOps best practices to easily deploy, monitor, and maintain AI models.
  • Ensure AI is reliable and fair by adding real-time fixes for errors, setting up rules and guardrails, and meeting industry regulations like those in Fintech.
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