Senior MLOps Engineer

Description

The team builds the machine learning platforms that take experimental NLP, information retrieval and GenAI models into production, and the work supports the company's scholarly search products, its AI features and its journal submission and peer review workflows.

The platforms draw on one of the largest scholarly corpora in the world, so the work covers GenAI, agentic AI, RAG, search and ranking quality, and knowledge graph-based retrieval, while protecting content rights and editorial confidentiality.

You will scope projects with managers and then carry them out on your own. You will also help mentor junior colleagues and help shape team best practices.

Responsibilities

• Automate and orchestrate ML workflows on AWS, Azure, Databricks and foundation model APIs such as OpenAI.
• Maintain model registries and artifact stores so that results can be reproduced 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 complete custom SageMaker pipelines for recommendation systems.
• Engineer the components of GAR+RAG systems, including query interpretation and reflection, chunking, embeddings, hybrid retrieval and semantic search. 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 for offline IR metrics such as NDCG, MAP and MRR, LLM quality measures such as faithfulness and grounding, and A/B tests.
• Reduce infrastructure costs through monitoring, scaling and more efficient resource use.
• Keep up with new GenAI, NLP and RAG research and apply the best approaches to experiments and production 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 in 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.
• A solid grasp 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

Flexible working hours, so you can arrange your day around when you work best.

The company offers country-specific benefits, and applicants with a disability or other need for accommodation can ask for adjustments during hiring.

The company is an equal opportunity employer that aims for a fair and accessible hiring process.

Benefits

• A comprehensive pension plan.
• Generous vacation allowance and the option of sabbatical leave.
• Maternity, paternity, adoption, family care and shared parental leave.
• Wellbeing initiatives and study assistance.
• Internal communities and networks.
• Various employee discounts.
• A recruitment referral reward.
• A global Employee Assistance Program.