AI/ML ENGINEER
3B Staffing LLC, Iselin, NJ, United States
AML Engineers
Required skillset - 4-8 years experience for the two levels, experience in the following is required
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 GCP but Azure or AWS are acceptable - will easily pick up GCP
Must be able to work within Kubernetes or similar
AI/ML skillset requirements:
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 (GCP, 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.
Day to Day
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.
Required skillset - 4-8 years experience for the two levels, experience in the following is required
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 GCP but Azure or AWS are acceptable - will easily pick up GCP
Must be able to work within Kubernetes or similar
AI/ML skillset requirements:
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 (GCP, 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.
Day to Day
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.