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.
Requirements
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)
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.
U.S. National Base Pay Range: $95,300 - $158,800. Geographic differentials may apply in some locations to better reflect local market rates.
If performed in Maryland, the base pay range is $100,100 - $166,800.If performed in New Jersey, the base pay range is $112,574 - $179,826.
This job is eligible for an annual incentive bonus.
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Benefits
Health insurancePerformance bonus
Additional Information
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.
Relx · Philadelphia, PA