Elsevier · 15 hours ago
Machine Learning Ops Engineer
Philadelphia, PA
Full-time
Onsite
Senior LevelElsevier is a renowned global information analytics company that primarily focuses on providing scientific, technical, and medical research content, tools, and services. In this role, you will bridge Data Science and Engineering to develop and manage AI-based features and machine learning workflows, ensuring they are secure, reliable, and scalable.
ContentContent DiscoveryDeliveryHealth CareInformation ServicesInformation TechnologyPublishing
Hiring Manager
ALAN KRULL
Responsibilities
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
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
Qualification
Required
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
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
Preferred
Background in health technology and/or medical content workflows is preferred
Company
Elsevier
Elsevier is a world-leading provider of information solutions that enhance the performance of science, health, and technology. It is a sub-organization of RELX.
Founded in 1880
5001-10000 employees
H1B Sponsorship
Elsevier has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (32)
2024 (17)
2023 (28)
2022 (46)
2021 (28)
2020 (19)
Funding
Current Stage
Late StageTotal Funding
unknown2003-09-01Private Equity
Leadership Team
Dan Olley
EVP & CTO - Elsevier
C
Catherine Thrift
CFO
Recent News
2025-12-18
Business Wire
2025-12-17
Research & Development World
2025-12-05
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