Bridge Data Science and Engineering to turn experimental NLP/IR/GenAI models into secure, reliable, and scalable services.
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 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.

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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

machine learningMLOpsPythonJavaScalaElasticsearchOpenSearchSolrPyTorchTensorFlow