Senior MLOps Engineer

RX Brasil; Greater London; On-site; GBP 90,000 - 150,000; Full time

Job summary

Elsevier is seeking an experienced ML Engineer in the UK to bridge data science and engineering, turning NLP/IR/GenAI experiments into secure, scalable services for one of the world’s largest scholarly corpora.

You will architect and implement ML pipelines, manage model registries, and work across AWS, Azure, and SageMaker. The role emphasizes production-grade MLOps, governance, and collaboration with Product Managers, Data Scientists, and Responsible AI teams.

Qualifications

• Extensive experience building ML/GenAI systems in production.
• Strong Python/Java/Scala engineering skills.
• Hands-on with cloud ML platforms and vector/graph DBs.

Responsibilities

• ML & LLM Engineering for search and recommendation engines.
• Automate ML workflows across AWS, Azure, and other platforms.
• Maintain model registries and governance for reproducibility.
• Design end-to-end SageMaker pipelines for recommendations.
• Develop GAR+RAG components and knowledge-graph retrieval.

Skills

Python

Java/Scala

ML Engineering

NLP

Spark

Cloud (AWS/Azure)

Elasticsearch/OpenSearch

Graph DBs

SageMaker

MLflow

Azure ML

OpenAI API

Bedrock

Tools

SageMaker

MLflow

Azure ML

OpenAI API

Bedrock

Job description

About the Team:Join the team that powers Elsevier’s research platforms—Scopus/Scopus AI, ScienceDirect/ScienceDirect AI, and journal submission & peer review workflows.About the Role: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 scholarly corpora, so 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.Responsibilities:ML & LLM Engineering, Search and Recommendation EnginesAutomate 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 governanceDevelop 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.End-to-end custom SageMaker pipelines for recommendation systems.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-hostedDesign and implement ML pipelines that utilize Elasticsearch/OpenSearch/Solr, vector DBs, and graph DBsBuild evaluation pipelines: offline IR metrics (e.g., NDCG, MAP, MRR), LLM quality metrics (e.g., faithfulness, grounding), and A/B testing.Optimize infrastructure costs through monitoring, scaling strategies, and efficient resource utilizationStay current with the latest GAI research, NLP and RAG and apply the state-of-the-art in our experiments and systemsCollaborationPartner with Subject-Matter Experts, Product Managers, Data Scientists and Responsible AI experts to translate business problems into cutting edge data science solutionsCollaborate and interface with Operations Engineers who deploy and run production infrastructure.Requirements:Extensive experience ML Engineering, MLOps platforms, shipping ML or search/GenAI systems to production.Strong Python, Java, and/or Scala engineeringExperience with statistical analysis, machine learning theory and natural language processingHands-on ‑ experience with major cloud vendor solutions (AWS, Azure and/or Google)Search/vector/graph technologies (e.g., Elasticsearch / OpenSearch / Solr/ Neo4j).Experience in evaluating LLM modelsBackground with scholarly publishing workflows, bibliometrics, or citation graphsA strong understanding of the Data Science Life Cycle including feature engineering, model training, and evaluation metricsFamiliarity with ML frameworks, e.g., PyTorch, TensorFlow, PySparkExperience with large scale data processing systems, e.g., SparkWork in a Way That Works for YouWe promote a healthy work/life balance across the organisation. We offer an appealing working prospect for our people. With numerous wellbeing initiatives, shared parental leave, study assistance, and sabbaticals, we will help you meet your immediate responsibilities and your long-term goals.Working PatternWorking flexible hours - flexing the times when you work in the day to help you fit everything in and work when you are the most productiveAbout the BusinessA global leader in information and analytics, we help researchers and healthcare professionals advance science and improve health outcomes for the benefit of society. Building on our publishing heritage, we combine quality information and vast data sets with analytics to support visionary science and research, health education and interactive learning, as well as exceptional healthcare and clinical practice. At Elsevier, your work contributes to the world's grand challenges and a more sustainable future. We harness innovative technologies to support science and healthcare to partner for a better worlWe 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.We are committed to providing a fair and accessible hiring process. If you have a disability or other need that requires accommodation or adjustment, please let us know by completing our Applicant Request Support Form or please contact 1-855-833-5120.Criminals may pose as recruiters asking for money or personal information. We never request money or banking details from job applicants. Learn more about spotting and avoiding scams here .Please read our Candidate Privacy Policy .We are an equal opportunity employer: qualified applicants are considered for and treated during employment 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.USA Job Seekers:EEO Know Your Rights .