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