Senior ML Ops Engineer

AI in this role

openaibedrockpytorchtensorflowmlflowsagemakerdatabricks

ragml-opsnlpai-safety

Are you a collaborative Machine Learning Ops Engineer looking to work for a mission driven global organization?

Are you looking to drive cutting edge products that have a true societal impact?

About the team, this team that powers Elsevier’s Health platforms: Clinical Key AI, Sherpath AI, and AI-driven automated clinical and content workflows. You will bridge Data Science and Engineering to turn experimental NLP/IR/GenAI models into secure, reliable, and scalable services. Our systems operate over one of the world’s largest medical and scholarly landscapes.

About the role, as a Senior Machine Learning Engineer you’ll work on AI-based features (GenAI, Agentic AI, RAG, etc.) search/ranking quality, and knowledge graph aware retrieval while enforcing content rights and editorial confidentiality.

Key Responsibilities

ML & LLM Engineering, Search and Recommendation Engines

Automate and orchestrate machine learning workflows across major cloud and AI platforms (AWS, Azure, Databricks, and foundation model APIs such as OpenAI).
Maintain and version model registries and artifact stores to ensure reproducibility and governance.
Develop and manage CI/CD for ML, including automated data validation, model testing, and deployment.
Implement ML Engineering solutions using popular MLOps platforms such as AWS SageMaker, MLflow, Azure ML.
Scale end-end custom Sagemaker pipelines.
Design and implement the engineering components of GAR+RAG systems (e.g., query interpretation and reflection, chunking, embeddings, hybrid retrieval, semantic search), manage prompt libraries, guardrails and structured output for LLMs hosted on Bedrock/SageMaker or self-hosted.
Design and implement ML pipelines that utilize Elasticsearch/OpenSearch/Solr, vector DBs, and graph DBs .
Build evaluation pipelines: offline IR metrics (NDCG, MAP, MRR), LLM quality metrics (faithfulness, grounding), and A/B testing.
Optimize infrastructure costs through monitoring, scaling strategies, and efficient resource utilization.
Stay current with the latest GAI research, NLP and RAG and apply the state-of-the-art in our experiments and systems.

Collaboration

Partner with Subject-Matter Experts, Product Managers, Data Scientists and Responsible AI experts to translate business problems into cutting edge data science solutions
Collaborate and interface with Operations Engineers who deploy and run production infrastructure.

Qualifications

Current experience in ML Engineering, MLOps platforms, shipping ML or search/GenAI systems to production.
Strong Python, Java, and/or Scala experience will be considered a plus.
Hands-on‑ experience with major cloud vendor solutions (AWS, Azure and/or Google)
Experience with Search/vector/graph technologies (e.g., Elasticsearch / OpenSearch / Solr / Neo4j).
Experience in evaluating LLM models.
A strong understanding of the Data Science Life Cycle including feature engineering, model training, and evaluation metrics.
Background in health technology and/or medical content workflows is preferred.
Familiarity with ML frameworks, e.g., PyTorch, TensorFlow, PySpark.
Experience with large-scale data processing systems, e.g., Spark.
Experience with statistical analysis, machine learning theory and natural language processing.

Elsevier is a renowned global information analytics company that primarily focuses on providing scientific, technical, and medical (STM) research content, tools, and services. It is one of the largest publishers of academic journals and scholarly literature in the world. Elsevier operates in various domains, including science, technology, medicine, social sciences, and more. They publish a vast number of peer-reviewed journals covering a wide range of disciplines. These journals act as platforms for researchers and academics to share their findings and contribute to the advancement of knowledge in their respective fields.



U.S. National Base Pay Range: $95,300 - $158,800. Geographic differentials may apply in some locations to better reflect local market rates.

If performed in Maryland, the base pay range is $100,100 - $166,800.If performed in New Jersey, the base pay range is $112,574 - $179,826.

This job is eligible for an annual incentive bonus.







We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location.

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How we rate this

Senior ML Ops Engineer at RELX rates 99 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.

Classification

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

RAGML OpsNLPAI SafetyOpenAIBedrockPyTorchTensorFlow

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 monitor a model once it's live, and how do you know it needs retraining?
What NLP problem have you worked on, and how did you measure whether it actually worked?
How do you think about the risk of an AI system in this kind of role failing silently?
Walk me through how you've used OpenAI in your day-to-day work.

Adapt your resume

List these exact terms on your resume: RAG, ML Ops, NLP, AI Safety, and OpenAI. 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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