Senior ML Ops Engineer: GenAI & Vector Search

RXinsider LTD.; Philadelphia (Philadelphia County); On-site; USD 150,000 - 230,000; Full time

Job summary

Elsevier is seeking a Senior Machine Learning Engineer to build AI-based features and knowledge-graph aware retrieval across Health platforms. You will work on GenAI, RAG, and related systems, with responsibilities spanning automation, model governance, and scalable ML pipelines.

The role emphasizes production-grade ML engineering, collaboration with cross-functional teams, and staying current with AI research to push cutting-edge capabilities in health tech content workflows.

Qualifications

• Experience shipping ML or GenAI systems to production.
• Strong Python, Java/Scala experience.
• Hands-on with major cloud vendor solutions (AWS, Azure and/or Google).
• Experience with Search/vector/graph technologies (Elasticsearch/OpenSearch/Solr/Neo4j).
• Experience evaluating LLM models.
• Understanding of the Data Science lifecycle.

Responsibilities

• Automate and orchestrate ML workflows across cloud platforms and foundation model APIs.
• Maintain model registries and artifacts for reproducibility and governance.
• Develop and manage CI/CD for ML, including data validation and deployment.
• Implement ML engineering solutions with MLOps tools like SageMaker, MLflow, Azure ML.
• Scale SageMaker pipelines end-to-end and manage resource usage.
• Design and implement GAR+RAG components and retrieval systems.
• Build evaluation pipelines for IR and LLM quality metrics.
• Collaborate with SMEs to translate problems into data science solutions.

Skills

Python

Java/Scala

MLOps

Cloud platforms

ML Engineering

NLP/LLM

Data processing

Tools

Elasticsearch/OpenSearch/Solr

Neo4j

PyTorch

TensorFlow

PySpark

Spark

Job description

Elsevier is seeking a Senior Machine Learning Engineer to build AI-based features and knowledge-graph aware retrieval across Health platforms. You will work on GenAI, RAG, and related systems, with responsibilities spanning automation, model governance, and scalable ML pipelines.

The role emphasizes production-grade ML engineering, collaboration with cross-functional teams, and staying current with AI research to push cutting-edge capabilities in health tech content workflows.