Senior ML Ops Engineer
$95.3k - $158.8kElsevier
Are you a collaborative Machine Learning Ops Engineer looking to work for a mission driven global organization? Are you looking to drive cutting edge products that have a true societal impact? 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.,
- Strong Python, Java, and/or Scala experience will be considered a plus.
- Hands-on‑ experience with major cloud vendor solutions (AWS, Azure and/or
- Experience in evaluating LLM models.
- A strong understanding of the Data Science Life Cycle including feature
- 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
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