Lead Machine Learning Engineer

Motion Recruitment; Raleigh (NC); On-site; USD 180,000 - 240,000; Full time

Job summary

Motion Recruitment in Raleigh, NC is seeking a Lead Machine Learning Engineer to architect scalable AI and agentic platform capabilities for a global AI platform powering research assistants, retrieval systems, and enterprise agent workflows. You will shape AI platform strategy, standards and governance to ensure reliable, scalable systems aligned with responsible AI principles.

You’ll guide architectural decisions, establish reusable components for model lifecycle, orchestrate tools and ensure

Qualifications

• 10+ years of production-grade ML systems experience.
• Experience with LLMs, generative AI, and RAG deployments.
• Strong Python engineering background.

Responsibilities

• Architect scalable AI platforms and define reference architectures across products.
• Design high-availability, low-latency inference platforms for global scale.
• Establish reusable components to support model lifecycle, deployment and monitoring.
• Architect multi-step, reasoning-driven agent systems and tool orchestration.
• Lead MCP servers governance and standardize tool integration and context management.
• Define guardrails, permissions and audit mechanisms for enterprise-safe AI.
• Set best practices for MLOps, CI/CD, observability and reliability.
• Mentor senior engineers and influence technical direction across teams.

Skills

Python

Kubernetes

Distributed systems

LLMs

Generative AI

RAG systems

MLOps

Tool orchestration

Vector databases

Leadership

Education

Master’s degree

Bachelor’s degree

Tools

AWS

Azure

GCP

Docker

Job description

Motion Recruitment is hiring a Lead Machine Learning Engineer in Raleigh, NC to help architect scalable AI and agentic platform capabilities for a global AI platform powering LLM-powered research assistants, retrieval systems, and enterprise-grade agent workflows. In this role, you will shape AI platform strategy, technical standards, and governance so the systems operate reliably at scale while aligning with responsible AI principles.

What you’ll do

• Architect scalable AI platforms, including defining a reference architecture for LLM, ML, and agent-based systems across products
• Design high-availability, low-latency inference platforms for global scale
• Establish reusable platform components to support the model lifecycle, deployment, and monitoring
• Architect multi-step, reasoning-driven agent systems
• Design orchestration patterns for tool use, API invocation, and structured function calling
• Lead implementation and governance of Model Context Protocol (MCP) servers to standardize tool integration and context management
• Define guardrails, permissions, and audit mechanisms for enterprise-safe AI systems
• Set best practices for MLOps, CI/CD, observability, and system reliability
• Embed Responsible AI principles across platform architecture
• Mentor senior engineers and influence technical direction across teams

Required experience and education

• 10+ years of experience with a Master’s degree, or 12+ years of experience with a bachelor’s degree
• 10+ years building production-grade ML systems at scale
• Extensive experience with LLMs, generative AI, and RAG systems in real-world deployments
• Proven expertise designing distributed systems in cloud environments (AWS, Azure, or GCP)
• Hands-on experience with Kubernetes, containerization, and scalable inference systems
• Experience designing agentic systems and tool orchestration frameworks
• Experience implementing or governing MCP servers or structured tool-calling architectures
• Strong Python engineering background
• Experience with vector databases and search systems
• Deep understanding of model evaluation, reliability, and monitoring
• Strong architectural judgment and systems thinking
• Leadership experience influencing technical direction across teams
• Strong communication skills and executive presence
• Experience mentoring senior engineers or leading cross-functional initiatives

Technologies

• LLMs, RAG systems, Python
• AWS, Azure, GCP
• Kubernetes, containerization
• Model Context Protocol (MCP)
• Vector databases, search systems
• MLOps, CI/CD, observability