Senior ML Ops & GenAI Platform Engineer

Remitly; 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 experiments into secure, scalable services powering Health platforms such as Clinical Key AI and Sherpath AI. You will own ML workflows across cloud platforms (AWS, Azure), manage pipelines, and implement GAR/RAG components, embeddings, and vector databases.

Collaborate with PMs, data scientists and Responsible AI experts to ship production-ready models while upholding content

Qualifications

• Current experience in ML Engineering and shipping ML or GenAI systems to production.

Responsibilities

• Automate and orchestrate ML workflows across major cloud platforms.
• Maintain and version model registries and artifact stores.
• Develop and manage CI/CD for ML, including data validation and deployment.
• Implement ML Engineering solutions with MLOps platforms.
• Scale end-to-end SageMaker pipelines.
• Design GAR+RAG systems and embeddings workflows.
• Design ML pipelines using Elasticsearch/OpenSearch/Solr and vector databases.
• Build evaluation pipelines with IR and LLM quality metrics.
• Optimize infrastructure costs and monitor performance.
• Stay current with GAI research and apply it to systems.

Skills

ML Engineering

MLOps

Cloud platforms

Python/Java/Scala

NLP/IR/GenAI

Goverance & reproducibility

News skills

Tools

AWS

Azure

Google Cloud

Elasticsearch/OpenSearch/Solr

SageMaker/MLflow/Azure ML

Job description

Elsevier is seeking a Senior Machine Learning Engineer to bridge Data Science and Engineering, turning NLP/IR/GenAI experiments into secure, scalable services powering Health platforms such as Clinical Key AI and Sherpath AI. You will own ML workflows across cloud platforms (AWS, Azure), manage pipelines, and implement GAR/RAG components, embeddings, and vector databases.

Collaborate with PMs, data scientists and Responsible AI experts to ship production-ready models while upholding content