Lead Machine Learning Engineer

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

Job summary

Motion Recruitment Partners LLC seeks a Senior Machine Learning Engineer to define and lead the architecture of scalable AI/ML and agentic systems across our global product portfolio. You will shape long-term AI platform strategy, establish technical standards, and mentor engineers across teams.

You will architect multi-step, reasoning-driven agent systems, design high-availability inference platforms, and set governance for MCP servers and tool integrations, while embedding Responsible AI

Qualifications

• 10+ yrs of experience with Master’s degree or 12+ yrs with bachelor.
• 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.
• Leadership/Demonstrated ability to influence technical direction across teams.
• Strong communication skills and executive presence.

Responsibilities

• Architect scalable AI platforms across a global product portfolio.
• Define reference architecture for LLM, ML, and agent-based systems.
• Design high-availability, low-latency inference platforms for global scale.
• Establish reusable platform components for model lifecycle, deployment, and monitoring.
• Lead Agentic AI & Tool Ecosystems and architect multi-step agent systems.
• Design orchestration patterns for tool use, API invocation, and structured function calling.
• Lead implementation and governance of MCP servers to standardize tool integration.
• Define guardrails, permissions, and audit mechanisms for enterprise-safe AI systems.
• Elevate engineering standards with best practices for MLOps, CI/CD, observability, and reliability.
• Embed Responsible AI principles across platform architecture.
• Mentor senior engineers and influence technical direction across teams.

Skills

Python

Distributed systems

LLM & RAG

Tool orchestration

MLOps

Education

Master's degree in Computer Science or related field

Tools

Kubernetes

Containerization

Vector databases

Job description

Our Client serves customers in over 150 countries with trusted legal, regulatory, and business information. Theyare transforming the legal industry through cutting-edge AI, scalable data platforms, and intelligent research systems that power high-stakes decision-making for legal professionals worldwide. TheirGlobal AI Platform Team builds the foundational infrastructure behind next-generation AI products, including LLM-powered research assistants, retrieval systems, and enterprise-grade agentic workflows.

We are seeking a Sr. Machine Learning Engineer to define and lead the architecture of scalable AI/ML and agentic systems across our global product portfolio. This is a senior technical leadership role for someone who thrives at the intersection of:

• Large-scale distributed ML systems
• LLM and RAG architectures
• Agentic AI frameworks and tool orchestration
• Enterprise platform engineering

You will shape the long-term AI platform strategy and establish technical standards that impact millions of users

What You’ll

• Do Architect Scalable AI Platforms
• Define reference architecture for LLM, ML, and agent-based systems across products
• Design high-availability, low-latency inference platforms for global scale
• le.Establish reusable platform components for model lifecycle, deployment, and monitori
• Lead Agentic AI & Tool Ecosystems
• 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
• Elevate Engineering Standards. 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

What We’re Looking

For Experience/Education requirement

• 10+ yrs of experience with Master’s degree or 12+ yrs of experience with bachelor 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
• Technical Strength/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/Demonstrated ability to influence technical direction across teams
• Strong communication skills and executive presence
• Experience mentoring senior engineers or leading cross-functional initiatives