高级机器学习运维工程师
Senior ML Ops Engineer
你是否是一位善于协作的机器学习运维工程师，希望加入一家以使命为导向的全球组织？
你是否希望推动具有真正社会影响力的尖端产品？
关于团队，该团队为爱思唯尔（Elsevier）的健康平台：Clinical Key AI、Sherpath AI 以及基于人工智能的自动化临床和内容工作流程提供支持。你将连接数据科学与工程，将实验性的自然语言处理/信息检索/生成式人工智能模型转化为安全、可靠且可扩展的服务。我们的系统运行在世界上最大的医疗和学术环境中。
关于职位，作为高级机器学习工程师，你将参与基于人工智能的功能（生成式人工智能、代理人工智能、RAG 等）、搜索/排序质量以及知识图谱感知的检索工作，同时确保内容版权和编辑保密性。
关键职责
机器学习与大语言模型工程、搜索与推荐引擎
- 在主要云平台和 AI 平台（AWS、Azure、Databricks 以及基础模型 API 如 OpenAI）上自动化和编排机器学习工作流。
- 维护和版本化模型注册表和工件存储，以确保可重复性和治理。
- 开发和管理机器学习的 CI/CD，包括自动化的数据验证、模型测试和部署。
- 使用流行的 MLOps 平台（如 AWS SageMaker、MLflow、Azure ML）实现机器学习工程解决方案。
- 扩展端到端的自定义 SageMaker 流水线。
- 设计并实现 GAR+RAG 系统的工程组件（例如查询解释和反思、分块、嵌入、混合检索、语义搜索），管理提示库、防护措施和 LLM 的结构化输出，这些 LLM 可以托管在 Bedrock/SageMaker 或自托管环境中。
- 设计并实现利用 Elasticsearch/OpenSearch/Solr、向量数据库和图数据库的机器学习流水线。
- 构建评估流水线：离线信息检索指标（NDCG、MAP、MRR）、大语言模型质量指标（忠实度、依据性）以及 A/B 测试。
- 通过监控、扩展策略和高效的资源利用优化基础设施成本。
- 跟踪最新的生成式人工智能研究、自然语言处理和 RAG，并将最前沿的技术应用到我们的实验和系统中。
协作
- 与领域专家、产品经理、数据科学家和负责任的人工智能专家合作，将业务问题转化为尖端的数据科学解决方案。
- 与负责部署和运行生产基础设施的运维工程师协作并进行接口交互。
资格要求
- 具有机器学习工程方面的实际经验，
查看英文原文
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., 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.
Qualifications
- 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.
- 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.
Elsevier is a renowned global information analytics company that primarily focuses on providing scientific, technical, and medical (STM) research content, tools, and services. It is one of the largest publishers of academic journals and scholarly literature in the world. Elsevier operates in various domains, including science, technology, medicine, social sciences, and more. They publish a vast number of peer-reviewed journals covering a wide range of disciplines. These journals act as platforms for researchers and academics to share their findings and contribute to the advancement of knowledge in their respective fields.
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.We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location.
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Originally posted on Himalayas