Lead Machine Learning Engineer

Motion Recruitment is hiring a Lead Machine Learning Engineer (Raleigh, NC onsite) to drive the architecture of scalable AI/ML and agentic systems for a global LLM-powered research and enterprise assistant platform.

Responsibilities

• Architect scalable AI platform capabilities for LLM, ML, and agent-based systems across products
• Define reference architecture spanning inference, deployment, monitoring, and system reliability
• Design high-availability, low-latency inference platforms for global scale
• Establish reusable platform components for model lifecycle, deployment, and monitoring
• Architect multi-step, reasoning-driven agent systems
• Create 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

Requirements

• Experience: 10+ years (Master’s degree) or 12+ years (bachelor degree)
• ML at scale: 10+ years building production-grade ML systems at scale
• AI fundamentals: extensive experience with LLMs, generative AI, and RAG systems in real-world deployments
• Distributed systems: proven expertise designing distributed systems in cloud environments (AWS, Azure, or GCP)
• Infrastructure: hands-on experience with Kubernetes, containerization, and scalable inference systems
• Agents: experience designing agentic systems and tool orchestration frameworks
• MCP / tool calling: experience implementing or governing MCP servers or structured tool-calling architectures
• Programming: strong Python engineering background
• Retrieval: experience with vector databases and search systems
• Quality & reliability: deep understanding of model evaluation, reliability, and monitoring
• Architecture: strong architectural judgment and systems thinking
• Leadership: demonstrated ability to influence technical direction across teams
• Communication: strong communication skills and executive presence
• Mentorship: 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