Lead ML Ops Engineer

Are you a motivated and collaborative Lead ML Ops Engineer eager to make a difference in a mission-driven global organization? Do you want to develop cutting-edge products that have a meaningful societal impact? Join our dynamic team that powers Elsevier's Health platforms, including Clinical Key AI and Sherpath AI, along with automated clinical and content workflows. In this role, you will connect Data Science with Engineering to transform experimental NLP, IR, and GenAI models into secure, reliable, and scalable services, overseeing one of the largest medical and scholarly landscapes globally. As a Lead ML Ops Engineer, you will focus on AI-driven features, search/ranking quality, and knowledge graph-aware retrieval while ensuring the integrity of content rights and editorial confidentiality. Key Responsibilities Automate and orchestrate machine learning workflows on major cloud platforms (AWS, Azure, Databricks) and APIs (e.g., OpenAI). Maintain model registries and artifact stores for reproducibility and governance. Develop and manage CI/CD pipelines for machine learning, including automated data validation, model testing, and deployment. Implement ML Engineering solutions using MLOps platforms like AWS SageMaker, MLflow, and Azure ML. Scale end-to-end custom SageMaker pipelines effectively. Design and implement components of GAR+RAG systems, including query interpretation, chunking, embeddings, and semantic search. Implement ML pipelines utilizing Elasticsearch, vector databases, and graph databases. Build evaluation pipelines for offline IR metrics (NDCG, MAP, MRR) and LLM quality metrics (faithfulness, grounding), including A/B testing. Optimize infrastructure costs through careful monitoring and efficient resource utilization. Stay updated with the latest advancements in GAI, NLP, and RAG, applying state-of-the-art methods in our systems. Collaboration Partner with Subject Matter Experts, Product Managers, Data Scientists, and Responsible AI experts to craft innovative data science solutions. Collaborate with Operations Engineers to deploy and maintain production infrastructure. Qualifications Current experience in ML Engineering and deploying ML or search/GenAI systems in production. Strong proficiency in Python, Java, and/or Scala is an advantage. Hands-on experience with major cloud solutions (AWS, Azure, or Google). Experience with search, vector, and graph technologies (e.g., Elasticsearch, OpenSearch, Solr, Neo4j). Familiarity with evaluating LLM models. A deep understanding of the Data Science Life Cycle, including feature engineering, model training, and evaluation metrics. Background in health technology and medical content workflows is preferred. Experience with ML frameworks like PyTorch, TensorFlow, and PySpark. Experience with large-scale data processing systems like Spark. Solid understanding of statistical analysis, machine learning theory, and natural language processing. Elsevier is a leading global information analytics company dedicated to providing scientific, technical, and medical research content, tools, and services. As one of the largest academic journal publishers, we offer a platform for researchers and academics to disseminate their findings and advance knowledge in various fields. Base Pay Range: $95,300 - $158,800. Geographic differentials may apply. Specific ranges for Maryland: $100,100 - $166,800; for New Jersey: $107,646 - $171,954. This position is eligible for an annual incentive bonus. We prioritize the well-being and happiness of our employees and offer country-specific benefits. We are committed to a fair and accessible hiring process. If you require accommodations, please inform us of your needs. Be aware that scammers may pose as recruiters. We never ask for money or personal information from applicants. Please read our Candidate Privacy Policy. We are an equal opportunity employer, considering all qualified applicants regardless of characteristics protected by law. Elsevier's mission is to benefit society through products that assist researchers, healthcare professionals, and various industries in making informed decisions and achieving better outcomes.

Created: 2026-03-04