Own end-to-end architecture and delivery of enterprise Generative AI solutions, including RAG, agentic workflows, LLM integration, evaluation, observability, security, and compliance. Lead model and platform selection, define scalable reference architectures, support pre-sales and client workshops, guide multiple delivery teams, mentor engineers, govern technical decisions, and remain hands-on with prototypes and production code. Balance cost, latency, accuracy, security, and business value across concurrent client engagements.

Design and optimize AI workflows and agentic solutions in ZBrain. Gather business requirements, translate them into AI solution designs and user stories, collaborate with engineers on APIs and integrations, test and refine AI agents, document solutions, support UAT and releases, and present findings to technical and business stakeholders. The role emphasizes prompt and context engineering, iterative AI development, software fundamentals, and continuous learning in generative AI.

Designs, builds, and maintains scalable cloud data platforms and production pipelines across Azure, Databricks, Snowflake, and SAP. Responsibilities include ingestion, ETL/ELT, Lakehouse and dimensional modeling, CI/CD with Databricks Asset Bundles and Azure DevOps, data quality and observability, performance optimization, documentation, and stakeholder collaboration. The role also includes technical architecture, client advisory, engineering leadership, mentoring, and delivery of production Generative AI systems.