Senior ML Ops Engineer

• 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 cutting edge data science solutions
• Collaborate and interface with Operations Engineers who deploy and run production infrastructure

• Current experience in ML Engineering, MLOps platforms, shipping ML or search/GenAI systems to production
• Strong Python, Java, and/or Scala experience
• 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
• 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

Background in health technology and/or medical content workflows

• Annual incentive bonus
• Country specific benefits
• Fair and accessible hiring process with accommodation support