Senior ML Engineer

Job Description We are seeking a Senior Machine Learning Engineer to help architect and scale the core AI platform powering next generation legal research and decision support products used globally. This role sits on the Global AI Platform team and focuses on building enterprise grade, scalable AI systems, including LLM powered applications, retrieval augmented generation (RAG), and agentic AI workflows. This is a senior technical leadership role, not a research or single model position. You will design reusable AI platform components, define architectural standards, and ensure AI systems are reliable, secure, and production ready in a highly regulated environment. The ideal candidate has deep experience building ML systems at scale and enjoys operating at the intersection of AI architecture, distributed systems, and platform engineering. Day to Day: u2022u2003Designing and owning the architecture for enterprise AI platforms used across multiple LexisNexis products u2022u2003Building and scaling LLM powered systems (including RAG pipelines) that support legal research and decision making tools u2022u2003Designing agentic AI workflows where models reason, call tools/APIs, and execute multi step tasks u2022u2003Creating high availability, low latency inference systems for global, enterprise users u2022u2003Establishing platform standards for model deployment, monitoring, evaluation, and reliability u2022u2003Defining guardrails, permissions, and auditability for AI systems in a regulated legal environment u2022u2003Working closely with product, platform, and engineering teams to ensure AI systems are reusable and scalable u2022u2003Mentoring senior engineers and influencing technical direction across teams u2022u2003Ensuring Responsible AI principles are embedded into system design (safety, reliability, governance) We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: Skills and Requirements u2022u200310+ years building production ML systems (not research only) u2022u2003Hands on LLM experience in production, including: ou2003RAG architectures ou2003Inference performance, reliability, and monitoring u2022u2003Experience designing agentic AI systems (models calling tools/APIs, multi step workflows) u2022u2003Strong distributed systems architecture experience in cloud environments (AWS, Azure, or GCP) u2022u2003Kubernetes + containerization experience in production environments u2022u2003Strong Python engineering background (platform level code, not just notebooks) u2022u2003Experience building or contributing to enterprise AI platforms used by multiple teams u2022u2003Proven ability to lead technically (set standards, mentor engineers, influence architecture) u2022u2003Comfortable working in regulated or high reliability environments u2022u2003Direct experience with Model Context Protocol (MCP) servers or structured tool calling frameworks u2022u2003Deep experience with vector databases and large scale search systems u2022u2003Experience designing LLMOps / MLOps standards at the platform level u2022u2003Prior work in legal, financial, healthcare, or other regulated industries u2022u2003Exposure to Responsible AI governance, auditing, or compliance frameworks u2022u2003Experience building internal AI platforms rather than just end user applications u2022u2003Background mentoring senior engineers or leading cross team technical initiatives

Created: 2026-04-24