DataScientist

We’re building AI products that move from promising prototypes to reliable, production-grade systems with measurable business impact. This role blends LLM application engineering, applied data science, and data engineering fundamentals. You will own the end-to-end data operations lifecycle: sourcing and understanding data, designing scalable pipelines, enabling analytics and model development, and partnering with engineering and data science to productionize solutions. You will translate complex data into context for humans and AI applications, implement durable processes. You’ll design, evaluate, deploy, and continuously improve GenAI systems (RAG, agentic workflows, model fine-tuning) while helping shape the data foundation that makes them trustworthy at scale. **Position Responsibilities:** * Design, evaluate, and deploy RAG pipelines, agentic systems, and chat interfaces, including advanced retrieval methods and modular designs. * Conduct LLM red-teaming, bias detection, and alignment testing; build automated evaluation frameworks and develop measurement/feedback loops. * GenAI engineering in the Azure ecosystem: integrate and test LLMs using OpenAI APIs, Azure AI Services, Azure AI Search/Cognitive Search, Azure ML, Databricks, and (where applicable) AKS/Kubernetes. * Develop and implement ML models (traditional + GenAI) and design/execute experiments to validate, optimize, and scale solutions. * Contribute to fine-tuning and pre-training pipelines for domain use cases; use synthetic data generation and creative data sourcing to improve modeling outcomes (including sparse/rare-event problems). **Data acquisition & domain understanding** * Identify, evaluate, and obtain access to internal and external data sources with minimal guidance. * Partner with subject matter experts to understand data definitions, quality, lineage, and business context. * Document datasets using standard templates (metadata, assumptions, refresh cadence, known issues) and present finding