Senior ML Ops Engineer

About the role

You will join the team that powers Elsevier’s Health platforms, including Clinical Key AI and Sherpath AI, bridging data science and engineering to turn experimental NLP, IR and generative AI models into secure, reliable, and scalable services. The role focuses on building and operating cutting‑edge ML and GenAI features that have a true societal impact in the medical and scholarly domains.

Key responsibilities

• Engineer ML and LLM pipelines, search and recommendation engines.
• Automate and orchestrate ML workflows across AWS, Azure, Databricks and foundation‑model APIs.
• Maintain model registries and artifact stores to ensure reproducibility and governance.
• Develop CI/CD pipelines for ML, including automated data validation, model testing and deployment.
• Build and scale custom SageMaker pipelines.
• Design GAR+RAG system components (query interpretation, chunking, embeddings, hybrid retrieval, semantic search) and manage prompt libraries, guardrails and structured LLM output.
• Implement ML pipelines using Elasticsearch/OpenSearch/Solr, vector and graph databases.
• Create evaluation pipelines with IR metrics (NDCG, MAP, MRR) and LLM quality metrics (faithfulness, grounding) and run A/B tests.
• Optimize infrastructure costs through monitoring, scaling strategies and efficient resource utilization.
• Stay current with GAI, NLP and RAG research and apply state‑of‑the‑art methods.

Required profile

• Proven experience in ML engineering and MLOps, delivering production ML or search/GenAI systems.
• Hands‑on experience with major cloud platforms (AWS, Azure, Google Cloud).
• Experience with search, vector and graph technologies such as Elasticsearch, OpenSearch, Solr, Neo4j.
• Experience evaluating large language models.
• Strong understanding of the data‑science lifecycle (feature engineering, model training, evaluation).
• Background in health technology or medical domain is a plus.
• Ability to collaborate with product, data‑science, responsible‑AI and operations teams.

Required skills

• Python
• Java
• Scala
• AWS SageMaker
• Azure ML
• Google Cloud AI Platform
• Databricks
• MLflow
• CI/CD tools (e.g., GitHub Actions, Jenkins)
• Elasticsearch / OpenSearch / Solr
• Neo4j or other graph DB
• Vector databases (e.g., Pinecone, FAISS)
• Foundation model APIs (OpenAI, Amazon Bedrock)
• Containerization (Docker, Kubernetes)
• Monitoring and cost‑optimization tools