Senior ML Ops Engineer for GenAI Health Systems

RELX; Philadelphia (Philadelphia County); On-site; USD 95,000 - 159,000; Full time

Job summary

Elsevier is seeking a Senior Machine Learning Engineer to bridge Data Science and Engineering, turning NLP/IR/GenAI models into scalable services across cloud platforms. You will work on AI-based features (GenAI, Agentic AI, RAG), search/ranking quality, and knowledge-graph aware retrieval while enforcing content rights and editorial confidentiality.

This role involves CI/CD for ML, model governance, GAR+RAG design, and collaboration with PMs, DS and Responsible AI experts to deliver

Qualifications

• Current experience in ML Engineering, MLOps platforms, shipping ML or search/GenAI systems to production.
• Hands-on experience with major cloud vendor solutions (AWS, Azure and/or Google).
• Experience with Search/vector/graph technologies (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.

Responsibilities

• 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.

Skills

ML Engineering

MLOps

Programming languages

Cloud platforms

Search/Vector/Graph

LLM evaluation

DS lifecycle

Health tech

ML frameworks

Spark

Tools

AWS SageMaker

MLflow

Azure ML

Databricks

Elasticsearch/OpenSearch/Solr

Neo4j

Job description

Elsevier is seeking a Senior Machine Learning Engineer to bridge Data Science and Engineering, turning NLP/IR/GenAI models into scalable services across cloud platforms. You will work on AI-based features (GenAI, Agentic AI, RAG), search/ranking quality, and knowledge-graph aware retrieval while enforcing content rights and editorial confidentiality.

This role involves CI/CD for ML, model governance, GAR+RAG design, and collaboration with PMs, DS and Responsible AI experts to deliver