Senior ML Ops Engineer: GenAI, RAG & Secure Retrieval

Remitly; Philadelphia (Philadelphia County); Presencial; USD 95 000 - 159 000; Tempo integral

Resumo da oferta

Elsevier is seeking a Senior Machine Learning Engineer to advance AI-based features across health platforms, including GenAI, RAG, and knowledge-graph informed retrieval. You will bridge Data Science and Engineering to build secure, scalable ML services used on a global medical content platform.

You will own end-to-end ML pipelines, collaborate with data scientists and engineers, implement MLOps practices, and optimize models and infrastructure while maintaining governance and confidentiality of

Qualificações

• Experience in ML engineering, MLOps, or shipping ML/GenAI systems to production.
• Proficiency with major cloud providers (AWS, Azure and/or Google).
• Experience with search, vector, graph technologies (Elasticsearch/OpenSearch/Solr, Neo4j).
• Familiar with ML frameworks (PyTorch, TensorFlow, PySpark).
• Experience evaluating LLM models and working with ML pipelines.

Responsabilidades

• Automate and orchestrate ML 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, including data validation and deployment.
• Design ML pipelines using Elasticsearch/OpenSearch/Solr, vector DBs, and graph DBs.
• Scale end-to-end Sagemaker pipelines and GAR+RAG components.

Conhecimentos

Python

Java

Scala

Elasticsearch/OpenSearch/Solr

Neo4j

Spark

Ferramentas

AWS

Azure

Google Cloud

Descrição da oferta de emprego

Elsevier is seeking a Senior Machine Learning Engineer to advance AI-based features across health platforms, including GenAI, RAG, and knowledge-graph informed retrieval. You will bridge Data Science and Engineering to build secure, scalable ML services used on a global medical content platform.

You will own end-to-end ML pipelines, collaborate with data scientists and engineers, implement MLOps practices, and optimize models and infrastructure while maintaining governance and confidentiality of