Senior ML Ops Engineer
你是一个合作的机器学习操作工程师,想为一个以使命驱动的全球组织工作吗?你想要推动具有真正社会影响的尖端产品吗?关于该团队,这个团队提供了 Elsevier 健康平台:临床关键 AI,Sherpath AI 和人工智能驱动的自动化临床和内容工作流程。你将桥梁数据科学与工程转换实验性 NLP/IR/GenAI 模型成为安全,可靠和可扩展性的服务。我们的系统在世界上最大的医疗和学术景观之一上运营。关于这个角色,作为高级机器学习工程师,你将在基于 AI 的功能 (GenAI,Agentic AI,RAG 等)上工作。同时执行内容权和编辑保密性,并对知识图表意识检索进行搜索/排名质量以及查询。关键责任 ML & LLM 工程,搜索和推引擎 · 自动化和调整机器学习的工作流程在主要云和人工智能平台上 (AWS,Azure,Databricks 和 OpenAI 等基础模型 API). ·维护版本模式注册表和文物存储以确保可复制性和治理。·开发和管理 ML 的 CI /CD,包括自动化的数据验证,模型测试和部署。· 通过使用 AWS SageMaker,MLflow,Azure ML 等流行的 MLOP 解决方案实施。· 设计和实现 GAR+RAG 系统的工程组件 (例如查询解释和反思,分量化,嵌入式,混合检索,语义搜索),管理 Bedrock/SageMaker 或自主托管 LLM 的快速库,护和结构性输出。· 开发并实现 Elasticsearch/OpenSearch/Solr,矢量 DB 和图形 DB 的 ML 管道。· 构建评估管道:离线测试指标 (NDCG,MAP,MRR),IRM 质量指标 (忠诚,地面)和 A/B. ·通过监测,扩展策略和高效资源利用来优化基础设施成本。·保持最新的 GAI 研究,NLP 和 RAG 信息并应用我们实验和系统中的最先进技术。合作 ·与主题专家,产品经理,数据科学家和负责人工智能专家合作,将业务问题转换为尖端的数据科学解决方案 · 与部署和运行生产基础设施的操作工程师进行协作和接口。资格 ·在 ML 工程方面现有经验,
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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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