Senior ML Ops Engineer — GenAI & Retrieval Systems

RXinsider LTD.; 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 research into secure, scalable services powering Clinical Key AI, Sherpath AI, and related workflows. You will work across cloud platforms, build end-to-end ML pipelines and contribute to knowledge graph–driven retrieval.

The role emphasizes collaboration with PMs and data scientists, owning ML engineering from experimentation to production, with a focus on performance, governance,

Qualifications

• Experience shipping ML or GenAI systems to production.
• Strong Python, Java and/or Scala skills.
• Hands-on with cloud vendor solutions (AWS/Azure/Google).
• Experience with search, vector, and graph technologies.
• Understanding of data science lifecycle: feature engineering, training, evaluation.
• Health technology or medical content workflows experience preferred.

Responsibilities

• Develop ML engineering solutions for search, ranking, and knowledge graphs.
• Automate ML workflows across cloud platforms and MLOps tools.
• Maintain model registries and CI/CD for ML deployments.
• Design GAR+RAG components including embeddings and retrieval.
• Build pipelines using Elasticsearch/OpenSearch/Solr and vector stores.
• Create evaluation pipelines with NDCG, MAP, MRR and A/B testing.

Skills

ML Engineering

MLOps

NLP/GenAI

Python

Java

Scala

Cloud (AWS/Azure/Google)

Elasticsearch/OpenSearch/Solr

PyTorch/TensorFlow

Spark

Graph databases

Tools

SageMaker

MLflow

OpenAI API

Bedrock

Keras

Job description

Elsevier is seeking a Senior Machine Learning Engineer to bridge Data Science and Engineering, turning NLP/IR/GenAI research into secure, scalable services powering Clinical Key AI, Sherpath AI, and related workflows. You will work across cloud platforms, build end-to-end ML pipelines and contribute to knowledge graph–driven retrieval.

The role emphasizes collaboration with PMs and data scientists, owning ML engineering from experimentation to production, with a focus on performance, governance,