Software Architect - Databricks

O projekcie: We're looking for Software Architect with proven experience architecting solutions on the Databricks Lakehouse Wymagania: - Proven experience architecting solutions on the Databricks Lakehouse using Unity Catalog, Delta Lake, MLflow, Model Serving, Feature Store, AutoML, and Databricks Workflows. - Expertise in real-time/low-latency model serving architectures with auto-scaling, confidence-based routing, and A/B testing. - Strong knowledge of cloud security and governance on Azure or AWS, including Azure AD/AWS IAM, encryption, audit trails, and compliance frameworks. - Hands-on MLOps skills across experiment tracking, model registry/versioning, drift monitoring, automated retraining, and production rollout strategies. - Proficiency in Python and Databricks-native tooling, with practical integration of REST APIs/SDKs and Databricks SQL in analytics products. - Familiarity with React dashboards and human-in-the-loop operational workflows for ML and data quality validation. - Demonstrated ability to optimize performance, reliability, and cost for large-scale analytics/ML platforms with strong observability. - Experience leading multi-phase implementations with clear success metrics, risk management, documentation, and training/change management. - Domain knowledge in telemetry, time-series, or industrial data (aerospace a plus) and prior work with agentic patterns on Mosaic AI. - Databricks certifications and experience in enterprise deployments of the platform are preferred. - English Level: B2/ C1 Codzienne zadania: - Lead the end-to-end architecture of a Databricks-centric multi-agent processing engine leveraging Mosaic AI, Model Serving, MLflow, Unity Catalog, Delta Lake, and Feature Store for decoder automation at scale. - Design governed data ingestion, storage, and real-time processing using Delta Lake, Structured Streaming, and Databricks Workflows with enterprise security and full lineage. - Own the model lifecycle with MLflow: experiment tracking, registry/versioning, A/B testing, drift monitoring, and automated retraining pipelines. - Architect low-latency model serving endpoints with auto-scaling and confidence-based routing for sub-second agent decisioning. - Establish robust data governance with Unity Catalog across all environments. - Drive performance and cost optimization - Define production release strategies (blue-green), monitoring and alerting, operational runbooks, and SLOs for dependable operations. - Partner with engineering, MLOps, and product to deliver human-in-the-loop workflows and dashboards using Databricks SQL and a React frontend. - Lead change management, training, and knowledge transfer while managing a parallel shadow-processing path during ramp-up. - Plan and coordinate phased delivery, success metrics, and risk mitigation across foundation, agent development, automation, and production rollout.