Description

You will drive the evolution of the company's MLOps platform by building scalable infrastructure and automated tools that empower data scientists to deploy and monitor models with high engineering rigor.

Requirements

• •5+ years experience in Spark, ML Ops, Data Engineering, AWS, Apache Airflow, Python
• •Experience productionizing ML models and orchestrating ML workflows
• •Experience with Databricks for scalable data processing and ML model training
• •Strong collaboration and communication skills
• •Bonus: Experience with feature stores and feature engineering pipelines

Tasks

• •Enhance the MLOps platform's scalability and efficiency
• •Develop and maintain Feature Store infrastructure
• •Manage Airflow pipelines and Databricks jobs
• •Build observability systems and telemetry pipelines
• •Design foundational components for data access
• •Create systems for model training and deployment
• •Develop automated model versioning and monitoring tools
• •Architect modular systems for cross-team ML development
• •Implement core abstractions and standardized APIs
• •Establish engineering frameworks and best practices
• •Collaborate with infrastructure and security teams
• •Ensure ML systems are secure and production-grade

Tools & Technologies

Benefits