Lead Machine Learning Engineer

About the job

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. 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