Senior ML Ops Engineer – GenAI, RAG & Scalable Pipelines

Elsevier; Philadelphia (Philadelphia County); On-site; USD 95,300 - 158,800; Full time

Job summary

Elsevier in the United States seeks a Senior Machine Learning Engineer to bridge Data Science and Engineering, delivering ML-based features across GenAI, RAG, and knowledge-graph aware retrieval. You will build scalable, secure services for health platforms and work closely with ML researchers and product teams.

You will design and implement end-to-end ML pipelines, maintain model registries, and optimize workflows across AWS/Azure/GCP. Strong Python/Java/Scala and cloud experience are required.

Qualifications

• Hands-on experience shipping ML or search/GenAI systems to production.
• Strong Python, Java, and Scala knowledge.
• Experience with major cloud providers (AWS, Azure, Google).
• Experience with search, vector, and graph technologies.
• Familiarity with ML frameworks (PyTorch, TensorFlow, PySpark).
• Background in health technology or medical content workflows is a plus.

Responsibilities

• Automate and orchestrate ML workflows across cloud and AI platforms.
• Maintain and version model registries and artifact stores.
• Develop and manage CI/CD for ML including data validation and testing.
• Design ML pipelines for vector DBs, graph DBs, and semantic search.
• Build evaluation pipelines with IR metrics and LLM quality metrics.
• Collaborate with cross-functional teams to translate problems into data solutions.

Skills

Python

Java

Scala

MLOps

GenAI

NLP

Cloud platforms

Elasticsearch

OpenSearch

Neo4j

PyTorch

TensorFlow

Tools

AWS SageMaker

MLflow

Azure ML

Databricks

Solr

Neo4j

Elasticsearch/OpenSearch

Job description

Elsevier in the United States seeks a Senior Machine Learning Engineer to bridge Data Science and Engineering, delivering ML-based features across GenAI, RAG, and knowledge-graph aware retrieval. You will build scalable, secure services for health platforms and work closely with ML researchers and product teams.

You will design and implement end-to-end ML pipelines, maintain model registries, and optimize workflows across AWS/Azure/GCP. Strong Python/Java/Scala and cloud experience are required.