ML OPs Engineer

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

About the team, this team that powers Elsevier’s Health platforms: Clinical Key AI, Sherpath AI, and AI-driven automated clinical and content workflows. You will bridge Data Science and Engineering to turn experimental NLP/IR/GenAI models into secure, reliable, and scalable services. Our systems operate over one of the world’s largest medical and scholarly landscapes.

About the role, as a Senior Machine Learning Engineer you’ll work on AI-based features (GenAI, Agentic AI, RAG, etc.) search/ranking quality, and knowledge graph aware retrieval while enforcing content rights and editorial confidentiality.

Key Responsibilities

ML & LLM Engineering, Search and Recommendation Engines

• Automate and orchestrate machine learning workflows across major cloud and AI platforms (AWS, Azure, Databricks, and foundation model APIs such as OpenAI).
• Maintain and version model registries and artifact stores to ensure reproducibility and governance.
• Develop and manage CI/CD for ML, including automated data validation, model testing, and deployment.
• Implement ML Engineering solutions using popular MLOps platforms such as AWS SageMaker, MLflow, Azure ML.
• Scale end-end custom Sagemaker pipelines.
• Design and implement the engineering components of GAR RAG systems (e.g., query interpretation and reflection, chunking, embeddings, hybrid retrieval, semantic search), manage prompt libraries, guardrails and structured output for LLMs hosted on Bedrock/SageMaker or self-hosted.
• 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 (faithfulness, grounding), and A/B testing.
• Optimize infrastructure costs through monitoring, scaling strategies, and efficient resource utilization.
• Stay current with the latest GAI research, NLP and RAG and apply the state-of-the-art in our experiments and systems.

Collaboration

• Partner with Subject-Matter Experts, Product Managers, Data Scientists and Responsible AI experts to translate business problems into cutting edge data science solutions
• Collaborate and interface with Operations Engineers who deploy and run production infrastructure.

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 (e.g., 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.

Elsevier is a renowned global information analytics company that primarily focuses on providing scientific, technical, and medical (STM) research content, tools, and services. It is one of the largest publishers of academic journals and scholarly literature in the world. Elsevier operates in various domains, including science, technology, medicine, social sciences, and more. They publish a vast number of peer-reviewed journals covering a wide range of disciplines. These journals act as platforms for researchers and academics to share their findings and contribute to the advancement of knowledge in their respective fields.