RELXPrincipal Machine Learning Engineer I
AI in this role
About our Team
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).
About the Role
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.
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.
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
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How we rate this
Principal Machine Learning Engineer I at RELX rates 96 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- How do you think about the risk of an AI system in this kind of role failing silently?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: RAG, AI Agents, and AI Safety. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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