Senior MLOps Engineer

Description

The role sits between data science and engineering: you turn experimental NLP, information retrieval and GenAI models into secure, dependable, scalable services.

The team works on research platforms built on one of the largest scholarly corpora in the world, covering AI features such as GenAI, agentic AI and RAG, search and ranking quality, and knowledge graph-aware retrieval.

Content rights and editorial confidentiality must be enforced throughout.

You scope projects with managers and carry them out on your own. You also help develop junior staff and contribute to best practices.

Senior team members are expected to handle basic methods independently and to take on more advanced approaches within their teams.

Responsibilities

• Automate and orchestrate ML workflows across major cloud and AI platforms, including AWS, Azure, Databricks and foundation model APIs such as OpenAI.
• Maintain model registries and artifact stores so that results are reproducible and governed.
• Build and run CI/CD for ML, covering automated data validation, model testing and deployment.
• Deliver ML engineering work on MLOps platforms such as AWS SageMaker, MLflow and Azure ML.
• Build end-to-end custom SageMaker pipelines for recommendation systems.
• Engineer GAR and RAG systems, including query interpretation and reflection, chunking, embeddings, hybrid retrieval and semantic search, and manage prompt libraries, guardrails and structured LLM output on Bedrock, SageMaker or self-hosted models.
• Build ML pipelines that use Elasticsearch, OpenSearch or Solr, vector databases and graph databases.
• Create evaluation pipelines covering offline IR metrics such as NDCG, MAP and MRR, LLM quality metrics such as faithfulness and grounding, and A/B tests.
• Reduce infrastructure costs through monitoring, scaling and efficient resource use.
• Keep up with recent GAI, NLP and RAG research and apply state-of-the-art methods to experiments and systems.
• Work with subject-matter experts, product managers, data scientists and Responsible AI specialists to turn business problems into data science solutions.
• Coordinate with operations engineers who deploy and run production infrastructure.

Requirements

• Extensive experience in ML engineering and MLOps, including shipping ML or search and GenAI systems to production.
• Strong engineering skills in Python, and/or Java or Scala.
• Experience with statistical analysis, machine learning theory and natural language processing.
• Hands-on experience with major cloud providers such as AWS, Azure and/or Google Cloud.
• Experience with search, vector or graph technologies such as Elasticsearch, OpenSearch, Solr or Neo4j.
• Experience evaluating LLMs.
• Background in scholarly publishing workflows, bibliometrics or citation graphs.
• Solid understanding of the data science lifecycle, including feature engineering, model training and evaluation metrics.
• Familiarity with ML frameworks such as PyTorch, TensorFlow and PySpark.
• Experience with large-scale data processing systems such as Spark.

Conditions

The company offers country-specific benefits, which are listed by location.

The company provides accommodation during the hiring process on request, and welcomes requests for adjustments.

Benefits

• Flexible working hours, so you can arrange your day around when you are most productive.
• Comprehensive pension plan.
• Generous vacation allowance and the option of sabbatical leave.
• Maternity, paternity, adoption, family care and shared parental leave.
• Study assistance and wellbeing initiatives.
• Internal communities and networks.
• Various employee discounts.
• Referral reward for successful recruitment introductions.
• Global employee assistance program.