Senior ML Ops Engineer

RELX

Senior ML Ops Engineer driving cutting-edge AI solutions for health platforms at Elsevier. Collaborating with cross-disciplinary teams to enhance machine learning workflows.

Posted 5/6/2026full-timeRemote • Connecticut, New Jersey, Virginia • 🇺🇸 United StatesSenior💰 $95,300 - $158,800 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureCloudElasticSearchJavaNeo4jPySparkPythonPyTorchScalaSparkTensorflow

About the role

Key responsibilities & impact

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
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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

This job is eligible for an annual incentive bonus
We are delighted to offer country specific benefits.

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Hard Skills & Tools

machine learningMLOpsCI/CDdata validationmodel testingAWS SageMakerMLflowElasticsearchPyTorchTensorFlow

Soft Skills

collaborationcommunicationproblem-solvinginterpersonal skills