Remote Principal AI Platform Architect for Enterprise AI

ZealHire Inc.; Northern (KY); Hybrid; USD 150,000 - 230,000; Full time

Job summary

ZealHire Inc. is seeking a Principal Machine Learning Engineer to lead the design and implementation of Agentic AI platforms, including LLM infrastructure, RAG systems, and production ML tooling. The role emphasizes hands-on engineering, scalable systems, and collaboration with data scientists and product teams.

You will own architecture decisions, guide MLOps and LLMOps, and drive reliability, performance, and cost optimization across enterprise AI deployments.

Qualifications

• Bachelor's, Master's, or PhD in Computer Science, Engineering, Data Science, or related field.
• 10+ years of software engineering or machine learning engineering experience.
• 5+ years building production ML systems.
• 3+ years building production GenAI platforms.
• Strong expertise in Python, SQL, Spark, distributed computing, Kubernetes, Docker.

Responsibilities

• Design and implement enterprise-grade agent frameworks supporting multi-agent collaboration, planner-executor architectures, tool-use workflows, memory systems, and human-review workflows.
• Build orchestration services and runtime environments for AI agents.
• Implement MCP and agent interoperability standards.
• Design state-sharing and context management systems.
• Lead architecture reviews and mentor engineering teams.

Skills

Python

SQL

Spark

Distributed computing

Kubernetes

Docker

Education

Bachelor's/Master's/PhD in CS or related field

Tools

LangGraph

LangChain

LlamaIndex

Vector databases

FastAPI

REST APIs

AWS

Azure

GCP

Job description

ZealHire Inc. is seeking a Principal Machine Learning Engineer to lead the design and implementation of Agentic AI platforms, including LLM infrastructure, RAG systems, and production ML tooling. The role emphasizes hands-on engineering, scalable systems, and collaboration with data scientists and product teams.

You will own architecture decisions, guide MLOps and LLMOps, and drive reliability, performance, and cost optimization across enterprise AI deployments.