Tech Stack

Description

You will design and scale the infrastructure supporting the GPU Intelligence Platform, building pipelines for data handling, feature processing, model training, and system optimization. Your work will transition prototypes to production environments, focusing on scalability and dynamic resource management to enhance system stability and performance.

Requirements

• 7+ years experience in data engineering or MLOps in production
• Proven experience building high-throughput streaming pipelines (Kafka, Pulsar, Kinesis)
• Strong experience with time-series feature pipelines and observability systems
• Hands-on experience deploying ML models to production with versioning and monitoring
• Proficiency in Python and at least one systems language (Go, Rust, C++)

Responsibilities

• Design and implement scalable data ingestion pipelines
• Build feature processing and baseline computation systems
• Productionize models for prediction and detection
• Architect horizontally scalable services with orchestration
• Implement monitoring and feedback loops for model improvement