Data Scientist / Applied ML Engineer

Responsibilities

• Design, build, and orchestrate AI workflows that combine live web search, LLM scoring, and structured reasoning pipelines
• Build and tune prompt systems for structured LLM outputs - scoring rubrics, reasoning generation, hallucination prevention
• Train, evaluate, and improve ML/deep learning models for prediction tasks using structured data
• Deploy ML models to production as inference endpoints/services, manage versioning and rollback, and continuously monitor model performance (drift, accuracy decay, data quality), retraining and updating models as needed
• Build async data collection pipelines that gather data from external sources in real time
• Monitor LLM behavior in production using observability tooling and continuously improve prompt quality
• Design scoring systems that are explainable and consistent
• Improve data quality by building smart deduplication, classification, and validation logic

Languages