Architect

Area(s) of responsibility

- Design and implement GenAI and Agentic AI solutions using Databricks Genie, Databricks Agent Bricks, Mosaic AI (model training, evaluation, prompt engineering, RAG)

- Experience Build LLM-powered use cases

- Design and build scalable data pipelines using Databricks, PySpark, and SQL

- Implement Delta Lake, Unity Catalog, and medallion architecture

- Optimize Spark jobs for performance, cost, and scalability

- Perform data ingestion, transformation, and enrichment from multiple sources

- Ensure data quality, reliability, and observability across pipelines

- Design and develop enterprise ontologies, taxonomies, vocabularies, and semantic data models.

- Support AI/ML workloads by preparing high‑quality, feature‑ready datasets

- Develop and fine-tune LLMs/ML models using Databricks MLflow and Mosaic AI

- Implement prompt engineering, model evaluation, and guardrails for enterprise GenAI use cases

- Integrate AI solutions with enterprise data and business workflows