Senior ML Engineer: GenAI/RAG for Health AI Platforms

Elsevier; Philadelphia (Philadelphia County); On-site; USD 140,000 - 190,000; Full time

Job summary

Elsevier seeks a Senior Machine Learning Engineer to bridge Data Science and Engineering for Health platforms, turning GenAI and retrieval experiments into secure, scalable services. You will help build AI-driven features, search/ranking quality, and knowledge-graph aware retrieval while enforcing content rights.

You will automate ML workflows across cloud platforms, maintain model registries, and drive CI/CD for ML with a focus on governance and cost optimization.

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 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.
• Experience with statistical analysis, machine learning theory and natural language processing.

Responsibilities

• Automate and orchestrate ML workflows across major cloud and AI platforms (AWS, Azure, Databricks, and OpenAI APIs).
• Maintain and version model registries and artifact stores for reproducibility and governance.
• Develop and manage CI/CD for ML, including automated data validation, model testing, and deployment.
• Implement ML Engineering solutions using MLOps platforms such as AWS SageMaker, MLflow, Azure ML.
• Scale end-to-end custom SageMaker pipelines.
• Design GAR+RAG systems (query interpretation, reflection, embeddings, hybrid retrieval, semantic search) and manage prompt libraries and guardrails.
• 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, and A/B testing.
• Optimize infrastructure costs through monitoring, scaling strategies, and efficient resource utilization.
• Stay current with GAI research, NLP and RAG and apply state-of-the-art in experiments and systems.
• Partner with Subject-Matter Experts, Product Managers, Data Scientists and Responsible AI experts to translate business problems into cutting edge data science solutions.
• Collaborate with Operations Engineers who deploy and run production infrastructure.

Skills

Python

Java

Scala

AWS

Azure

Elasticsearch/OpenSearch/Solr

Neo4j

PyTorch

TensorFlow

Spark

LLM evaluation

Tools

OpenAI API

SageMaker

MLflow

Bedrock

Job description

Elsevier seeks a Senior Machine Learning Engineer to bridge Data Science and Engineering for Health platforms, turning GenAI and retrieval experiments into secure, scalable services. You will help build AI-driven features, search/ranking quality, and knowledge-graph aware retrieval while enforcing content rights.

You will automate ML workflows across cloud platforms, maintain model registries, and drive CI/CD for ML with a focus on governance and cost optimization.