Job Description
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...