Senior ML Ops Engineer

Job not on LinkedIn

🕒 May 6

🦌 Connecticut, New Jersey, +1 more states – Remote

🦌 Connecticut – Remote 🏖️ New Jersey – Remote ⚔️ Virginia – Remote

💵 $95.3k - $158.8k / year

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

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RELX

WebsiteLinkedInAll Job Openings

10,000+ employees

🏢 Enterprise

🔬 Science

Enterprise • Science • Legal

RELX is a global provider of information-based analytics and decision tools for professional and business customers. The company focuses on enabling its clients to make better decisions, improve results, and enhance productivity by leveraging advanced technology and data. RELX serves various sectors, including Risk, Scientific, Technical & Medical, Legal, and Exhibitions, by offering specialized information and analytical tools that facilitate critical decision-making. The company is committed to corporate responsibility and delivering societal benefit through its products by contributing to scientific advancement, legal justice, and effective market transactions.

📋 Description

• Automate 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 governance. • Develop 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. • Scale end-end custom Sagemaker pipelines. • 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-hosted. • Design and implement ML pipelines that utilize Elasticsearch/OpenSearch/Solr, vector DBs, and graph DBs. • Build evaluation pipelines: offline IR metrics (NDCG, MAP, MRR), LLM quality metrics (faithfulness, grounding), and A/B testing. • Optimize infrastructure costs through monitoring, scaling strategies, and efficient resource utilization. • Stay current with the latest GAI research, NLP and RAG and apply the state-of-the-art in our experiments and systems. • Partner with Subject-Matter Experts, Product Managers, Data Scientists and Responsible AI experts to translate business problems into data science solutions. • Collaborate and interface with Operations Engineers who deploy and run production infrastructure.

🎯 Requirements

• Current experience in ML Engineering, MLOps platforms, shipping ML or search/GenAI systems to production. • Strong Python, Java, and/or Scala experience will be considered a plus. • Hands-on experience with major cloud vendor solutions (AWS, Azure and/or Google) • Experience with Search/vector/graph technologies (e.g., Elasticsearch / OpenSearch / Solr / Neo4j). • Experience in evaluating LLM models. • A strong understanding of the Data Science Life Cycle including feature engineering, model training, and evaluation metrics. • Background in health technology and/or medical content workflows is preferred. • Familiarity with ML frameworks, e.g., PyTorch, TensorFlow, PySpark. • Experience with large-scale data processing systems, e.g., Spark. • Experience with statistical analysis, machine learning theory and natural language processing.

🏖️ Benefits

• Country specific benefits • Annual incentive bonus