Machine Learning Engineer Lead
AI summary of the role
Lead a team of 4-5 ML engineers to design, build, and operate scalable AI/ML systems and agentic architectures for next-generation legal research and analytics products.
What you’ll do
- Lead, mentor, and grow a team of 4-5 ML engineers
- Architect distributed ML systems serving multiple global products
- Define and implement enterprise-ready agentic frameworks and multi-step reasoning systems
- Develop evaluation frameworks to measure reasoning quality, hallucination rates, and reliability
What you’ll bring
- 8-10 years of Machine Learning/Software Engineer experience
- 2-3 years of people management experience
- Strong software engineering background with experience in building system design and architecting AI features/products for large user bases and unstructured data
- Experience with ML deployment to production
Technologies
ML · LLM · agentic frameworks · CI/CD · distributed systems · retrieval systems · guardrails · evaluation frameworks
About LexisNexis Legal & Professional
Global provider of legal, regulatory and business information powering AI-enabled research, drafting and analytics for law firms, corporate legal, courts and government.
Public · 5000+ people
Source and classification
Deployment team leadership · Evidence for this classification:
you love collaborating with teams to solve complex technical problems? We are seeking a Machine Learning Engineer Lead to design, build, and operate scalable AI/ML systems and agentic architectures that support next-generation legal research and analytics products. This role combines deep ML expertise with distributed systems engineering and AI platform development. In this role you will be a hands-on engineer and leader that will lead a high-performing team of 4-5 ML engineers, drive platform-level decisions, and ensure enterprise-grade scalability, reliability, and responsible AI compliance. Responsibilities: Lead, mentor, and grow a team of 4-5 ML engineers. Provide architectural direction and code-level guidance. Establish engineering best practices for ML system design, testing, and deployment. Conduct design reviews, performance reviews, and technical roadmap planning.How jobs are selected
Employer postings · Data from · Sources