Optimize, standardize and implement data science and machine learning solutions at scale and in cloud-based environments (Azure)
Participate in the end-to-end lifecycle of data science projects through the use of DevOps, code, experiment and model management, CI/CD and further industry best practices
Write well-designed, testable, efficient code
Work closely with engineering to continuously improve the way we consume data and deploy models in production
Design and lead on monitoring, troubleshooting, debugging and incident management for our ML pipelines
Be a trusted advisor and evangelist to the team and stakeholders on various aspects of ML Ops, from scaling and throughput, to infrastructure and deployment strategies
requirements-expected :
2+ years of professional experience in ML Ops or ML engineering, particularly in productionizing and scaling ML models
Business-ready command of English, written and spoken
Broad familiarity with Azure cloud environment and Databricks, including its setup and maintenance as an ML platform
Proven experience with software development best practices including testing, continuous integration, and DevOps tools
Advanced proficiency with Python and SQL in version control systems (git) plus their use in building ML & data pipelines
Good understanding of data science lifecycle and the way data scientists work to deliver value
Familiarity with agile software development lifecycle (SCRUM, Kanban, etc.)
Attention to clarity of code, ease of development, and correctness of implementations
