The role involves supporting, maintaining, and optimizing existing Databricks-based data applications and production pipelines. You will collaborate with engineering teams to ensure system reliability, performance, and scalability while managing workspace configurations and data quality.
Design, develop, and maintain enterprise-grade Power BI reports, dashboards, and semantic models to meet business requirements. Integrate Python for advanced data processing and collaborate with stakeholders to ensure reliable delivery of insights.
You will lead cloud infrastructure design and migration efforts on AWS while developing scalable Django-based applications. Additionally, you will act as a technical advisor to stakeholders and mentor junior engineers to ensure secure, cost-efficient, and high-performance cloud solutions.
You will own the roadmap for AI-powered product areas, translating ambiguous business problems into clear requirements and success metrics. You will collaborate with engineering and data science teams to ship production-ready AI features while managing tradeoffs between quality, cost, and user experience.
Lead cloud infrastructure design and migration efforts on AWS while developing scalable Django-based applications. Act as a technical advisor to stakeholders and mentor junior engineers while championing engineering best practices.
Design, develop, and deploy end-to-end machine learning models and pipelines to drive business value. Collaborate with cross-functional teams to implement MLOps practices and optimize model performance in production.
Design and own the architecture of Databricks-based data platforms, including lakehouse design and medallion architecture. Lead complex data engineering initiatives and integrate AI/ML capabilities such as RAG and feature engineering pipelines.
Define and lead the long-term technical vision and architecture for the organization's AI/ML platform and MLOps capabilities. Solve complex technical problems and mentor senior engineers to ensure scalable, enterprise-grade ML systems.
Design, build, and operate cloud infrastructure and DevOps workflows on AWS and Azure. Drive CI/CD maturity, manage containerized workloads, and optimize cloud costs and security.
