Lead Machine Learning Engineer

The Lead Machine Learning Engineer will lead architecture for scalable AI and agentic systems powering a global AI platform for LLM-powered research assistants, retrieval systems, and enterprise-grade agent workflows. This role focuses on platform strategy, technical standards, governance, and responsible AI principles for large-scale deployment.

Key Responsibilities

• 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 to support global scale.
• Establish reusable platform components covering 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 Qualifications

• 10+ years of experience with a Master’s degree, or 12+ years of experience with a 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 such as 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 foundation.
• Experience with vector databases and search systems.
• Deep understanding of model evaluation, reliability, and monitoring.
• Strong architectural judgment and systems thinking.
• Leadership and demonstrated ability to influence 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

Location and Work Setup

Raleigh, NC (onsite)

Education

Master’s degree or bachelor degree