Principal Machine Learning Engineer I
About The Position
LexisNexis Legal & Professional, which serves customers in more than 150 countries with 11,800 employees worldwide, is part of RELX (www.relx.com), a global provider of information-based analytics and decision tools for professional and business customers. Our company has been a long-time leader in deploying AI and advanced technologies to the legal market to improve productivity and transform the overall business and practice of law, deploying ethical and powerful generative AI solutions with a flexible, multi-model approach that prioritizes using the best model from today’s top model creators for each individual legal use case. The company employs over 2,000 technologists, data scientists, and experts to develop, test, and validate solutions in line with RELX Responsible AI Principles (https://stories.relx.com/responsible-ai-principles/index.html). Do you love collaborating with teams to solve complex technical problems? We are seeking a Principal Machine Learning Engineer 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. You will play a key role in developing enterprise-grade AI systems, including large language model (LLM) infrastructure, retrieval-augmented generation (RAG) pipelines, and autonomous agent frameworks designed for complex large unstructured data.
Requirements
- 10 + years of Machine Learning/Software Engineer experience
- Master’s degree or bachelor's degree, computer science degree is highly desirable.
- Strong software engineering background with experience in building system design, architecting AI feature/products that caters large number of users and deals with large volume of unstructured data
- Experience with ML deployment to production
Responsibilities
- 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.
- Architect distributed ML systems serving multiple global products.
- Standardize infrastructure patterns for LLM serving and retrieval systems.
- Define and implement enterprise-ready agentic frameworks.
- Architect multi-step reasoning systems.
- Lead decisions on deterministic workflows vs. autonomous agents.
- Implement guardrails, safety layers, and traceability mechanisms.
- Develop evaluation frameworks to measure reasoning quality, hallucination rates, and reliability.
- Establish CI/CD standards for ML lifecycle management.
- Ensure compliance with enterprise data governance and responsible AI standards.
Benefits
- annual incentive bonus
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What This Job Offers
Job Type
Full-time
Career Level
Principal
