Architect ML Engineer: Lead Agentic AI Systems

Quantiphi; United States; Hybrid; USD 170,000 - 250,000; Full time

Job summary

Quantiphi is seeking an experienced Architect Machine Learning Engineer to design, build, and deploy production-grade agentic AI systems and multi-agent workflows from the ground up. The role focuses on autonomous reasoning, scalable architectures, and enterprise cloud deployment across major providers.

The candidate will implement memory, context engineering, and advanced tooling for agent execution, with strong emphasis on MLOps, CI/CD, and observable systems in production environments.

Qualifications

• 6-8 years of hands on experience in machine learning and AI engineering with proven track record of taking ML systems to production.
• Demonstrated expertise in building multi-agent systems and agentic workflows, preferably with Langraph/CrewAI.
• Proficiency in Python and ML frameworks; strong experience with FastAPI, async programming and microservices.
• Experience with vector databases (Pinecone/Weaviate/ChromaDB) and building scalable RAG systems.

Responsibilities

• Architect & Build Agentic Systems: design end-to-end multi-agent systems from scratch and define communication protocols.
• Engineer Advanced Agent Capabilities: develop custom agent-tools and specialized agent-skills for complex tasks.
• Pioneer Context Engineering: implement memory and stateful context management for autonomous decision making.
• Deploy Production-Grade Solutions: own deployment, scaling and maintenance on AWS/GCP/Azure with MLOps.
• Integrate and Optimize LLMs: apply RAG and PEFT to optimize autonomous reasoning engines.
• Design tool libraries and integrations: create APIs, database queries, and external service connections.
• Build and maintain RAG systems: use vector stores like Pinecone/Weaviate/ChromaDB for retrieval-augmented workflows.
• Ensure observability and cost control: monitor performance, latency, and reliability; implement KPIs.

Skills

Python proficiency

ML frameworks

FastAPI

Async programming

Microservices

Vector databases

LangSmith

Cloud platforms

Terraform

CI/CD

Docker/Kubernetes

Prompt engineering

Tool-calling agents

Langraph / CrewAI / AutoGen

GCP/AWS/Azure

Tools

Pinecone

Weaviate

ChromaDB

CrewAI

Langgraph

AutoGen

LangSmith

GitHub Actions

Jenkins

Terraform

CloudFormation

Docker

Kubernetes

TensorFlow

PyTorch

Transformers

Job description

Quantiphi is seeking an experienced Architect Machine Learning Engineer to design, build, and deploy production-grade agentic AI systems and multi-agent workflows from the ground up. The role focuses on autonomous reasoning, scalable architectures, and enterprise cloud deployment across major providers.

The candidate will implement memory, context engineering, and advanced tooling for agent execution, with strong emphasis on MLOps, CI/CD, and observable systems in production environments.