Lead Machine Learning Engineer

Our Client serves customers in over 150 countries with trusted legal, regulatory, and business information. They are 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. Their Global AI Platform Team builds the foundational infrastructure behind next-generation AI products, including LLM-powered research assistants, retrieval systems, and enterprise-grade agentic workflows. Role 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 Posted By: Kelly Ethridge