17 hours ago
London, United KingdomSenior
Responsibilities
- Automate and orchestrate machine learning workflows across AWS, Azure, Databricks, and foundation model APIs.
- Maintain and version model registries and artifact stores for reproducibility and governance.
- Develop CI/CD processes for ML, including data validation, model testing, and deployment.
- Build and manage ML Engineering solutions using AWS SageMaker, MLflow, and Azure ML.
- Design custom SageMaker pipelines for recommendation systems.
- Implement engineering components for GAR and RAG systems, including query interpretation, chunking, embeddings, hybrid retrieval, semantic search, prompt libraries, guardrails, and structured LLM output.
- Build ML pipelines using Elasticsearch, OpenSearch, Solr, vector databases, and graph databases.
- Create evaluation pipelines using offline information-retrieval metrics, LLM quality metrics, and A/B testing.
- Optimize infrastructure costs through monitoring, scaling strategies, and efficient resource utilization.
- Partner with subject-matter experts, product managers, data scientists, Responsible AI experts, and operations engineers to deliver production data science solutions.
Requirements
- Extensive experience in ML Engineering, MLOps platforms, and shipping ML, search, or GenAI systems to production.
- Strong engineering skills in Python, Java, and/or Scala.
- Experience with statistical analysis, machine learning theory, and natural language processing.
- Hands-on experience with major cloud platforms, including AWS, Azure, and/or Google.
- Experience with search, vector, and graph technologies such as Elasticsearch, OpenSearch, Solr, and Neo4j.
- Experience evaluating LLM models.
- Background in scholarly publishing workflows, bibliometrics, or citation graphs.
- Strong understanding of the data science lifecycle, including feature engineering, model training, and evaluation metrics.
- Familiarity with PyTorch, TensorFlow, PySpark, and large-scale data processing systems such as Spark.
Benefits
- Flexible working hours.
- Wellbeing initiatives and work/life balance support.
- Shared parental leave.
- Study assistance.
- Sabbaticals.
- Country-specific benefits and accommodations during the hiring process.
Tech Stack
Categories
About Elsevier
Elsevier builds scientific and medical information products and analytics platforms for researchers, clinicians, and educators. Its portfolio includes journals and databases such as The Lancet, Cell, ScienceDirect, Scopus, and ClinicalKey, sold primarily via institutional subscriptions, licensing, and open-access publishing fees. Founded in 1880 and headquartered in Amsterdam, Elsevier operates as a business of RELX, serving universities, hospitals, corporations, and government research agencies worldwide.