Senior ML Ops Engineer

About the Role Senior ML Ops Engineer to help power Elsevier Health platforms with AI-based features, bridging Data Science and Engineering to deploy secure, scalable ML/NLP/GenAI services across cloud platforms. You will focus on GenAI, RAG, search and knowledge graph retrieval while respecting content rights and editorial confidentiality. What You'll Do

• Automate and orchestrate machine learning workflows across AWS, Azure, Databricks, and foundation model APIs (OpenAI).
• Maintain and version model registries and artifact stores for reproducibility and governance.
• Develop and manage CI/CD for ML including automated data validation, model testing, deployment.
• Implement ML Engineering solutions using AWS SageMaker, MLflow, Azure ML.
• Scale end-to-end SageMaker pipelines.
• Design and implement GAR+RAG system components (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 using 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 experiments and systems.
• Collaborate 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. What We're Looking For
• Current experience in ML Engineering, MLOps platforms, shipping ML or search/GenAI systems to production.
• Strong Python, Java, and/or Scala experience.
• 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 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. Nice to Have
• Optional/preferred qualifications (only if mentioned) Compensation & Benefits
• U.S. National Base Pay Range: $95,300 - $158,800. Geographic differentials may apply. Maryland: $100,100 - $166,800. New Jersey: $112,574 - $179,826.
• This job is eligible for an annual incentive bonus.
• Country-specific benefits. Click here to access benefits specific to your location.
• Equal opportunity employer: qualified applicants are considered without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law.