Requirements:
Strong experience with Databricks (Workflows, MLflow, Delta Lake), Apache Spark (batch and streaming), and advanced Python (production-quality code).
Hands-on experience with streaming and real-time data systems.
Proven experience designing and implementing CI/CD pipelines.
Strong understanding of the ML lifecycle (training, deployment, monitoring, and retraining) and building scalable, distributed data and ML pipelines.
Experience with Snowflake, Kubernetes, and Docker.
Experience with Terraform or other Infrastructure as Code (IaC) tools.
Experience with feature stores (e.g., Snowflake Feature Store, Databricks Feature Store) and event-driven architectures (e.g., Kafka).
Experience with model serving frameworks, low-latency API development, and LLM deployment/serving.
Experience with monitoring and observability tools (e.g., ELK stack or similar).
Familiarity with A/B testing and experimentation frameworks.
Strong knowledge of...