Senior ML Ops Engineer: GenAI, RAG & Search

Elsevier; Philadelphia (Philadelphia County); On-site; USD 120,000 - 180,000; Full time

Job summary

Elsevier seeks a Senior Machine Learning Engineer to bridge Data Science and Engineering for secure, scalable AI-driven workflows across health platforms. You’ll develop AI-based features, improve search/ranking quality, and work on knowledge-graph aware retrieval while upholding content rights.

You’ll collaborate with SMEs, PMs, and Responsible AI teams to translate problems into cutting-edge data science solutions and deploy them in production across cloud environments.

Qualifications

• 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 (Elasticsearch/OpenSearch/Solr, Neo4j).
• Experience in evaluating LLM models.
• A solid understanding of the Data Science Life Cycle including feature engineering, model training, and evaluation metrics.
• Health tech/medical content workflows background is preferred.
• Familiarity with ML frameworks like PyTorch, TensorFlow, PySpark.
• Experience with large-scale data processing systems, e.g., Spark.
• Knowledge of NLP, ML theory, and statistical analysis.

Responsibilities

• Automate and orchestrate machine learning workflows across major cloud and AI platforms (AWS, Azure, Databricks, OpenAI).
• Maintain and version model registries and artifact stores for reproducibility and governance.
• Develop and manage CI/CD for ML with automated data validation, model testing, and deployment.
• Implement ML Engineering solutions using MLOps platforms like AWS SageMaker, MLflow, Azure ML.
• Scale end-to-end SageMaker pipelines and GAR/RAG systems components.
• Handle query interpretation, embeddings, hybrid retrieval, and semantic search with LLMs on Bedrock/SageMaker or self-hosted.
• Design ML pipelines using Elasticsearch/OpenSearch/Solr, vector DBs, and graph DBs.
• Build evaluation pipelines with offline IR metrics and LLM quality metrics; run A/B tests.
• Optimize infrastructure costs through monitoring, scaling, and resource management.
• Stay current with GAI research, NLP, and RAG; apply state-of-the-art to our systems.

Skills

Python

Java

Scala

Cloud platforms

ML Engineering

MLOps

LLM evaluation

NLP

Spark

PyTorch

TensorFlow

PySpark

Tools

Elasticsearch/OpenSearch/Solr

Neo4j

Job description

Elsevier seeks a Senior Machine Learning Engineer to bridge Data Science and Engineering for secure, scalable AI-driven workflows across health platforms. You’ll develop AI-based features, improve search/ranking quality, and work on knowledge-graph aware retrieval while upholding content rights.

You’ll collaborate with SMEs, PMs, and Responsible AI teams to translate problems into cutting-edge data science solutions and deploy them in production across cloud environments.