Senior ML Architect for Agentic AI & Multi-Agent Systems

Quantiphi, Inc.; Princeton (NJ); On-site; USD 140,000 - 190,000; Full time

Job summary

Quantiphi, Inc. in New Jersey seeks an Architect Machine Learning Engineer to design, build, and deploy production-grade agentic AI systems and multi-agent workflows from the ground up.

You will create scalable agentic architectures, integrate LLMs, and ensure enterprise-grade deployment on AWS, GCP, or Azure with robust MLOps practices.

The ideal candidate has 6–8 years in ML engineering, strong Python, and hands-on experience with vector databases and modern AI tooling.

Qualifications

• 6-8 years of hands on experience in machine learning and AI engineering with proven track record of taking ML systems to production.
• Experience building multi-agent systems and agentic workflows, preferably with Langraph/CrewAI
• Expertise in Python and ML frameworks; strong knowledge of FastAPI, async programming, and microservices architecture

Responsibilities

• Architect and build end-to-end multi-agent systems from scratch with agent harnesses and communication protocols
• Engineer advanced agent capabilities and define specialized agent-skills
• Pioneer context engineering and memory systems for persistent agent interactions
• Deploy production-grade solutions on AWS, GCP, or Azure with CI/CD and observability
• Integrate and optimize LLMs as core reasoning engines for autonomous agents
• Create and maintain tool libraries for agents including API integrations and DB queries
• Design and implement RAG systems using vector databases like Pinecone/Weaviate/ChromaDB

Skills

Python

TensorFlow

PyTorch

Transformers

FastAPI

Async programming

Microservices

Langraph

CrewAI

Vector databases

Pinecone

Weaviate

ChromaDB

Cloud platforms

Tools

Pinecone

Weaviate

ChromaDB

LangSmith

Arize AI

Job description

Quantiphi, Inc. in New Jersey seeks an Architect Machine Learning Engineer to design, build, and deploy production-grade agentic AI systems and multi-agent workflows from the ground up.

You will create scalable agentic architectures, integrate LLMs, and ensure enterprise-grade deployment on AWS, GCP, or Azure with robust MLOps practices.

The ideal candidate has 6–8 years in ML engineering, strong Python, and hands-on experience with vector databases and modern AI tooling.