Delivers machine learning ops engineering tasks such as deployment, implementation, optimization, and maintenance of machine learning pipelines and models.
Ensures pipelines support efficient data ingestion, preprocessing, model training, validation, deployment and monitoring.
Implements scalable and robust machine learning solutions that can handle large volumes of data and complex models.
Implements real-time inference with high availability and low latency.
Creates strategic plans within span of control and implements them across one to two business domains.
Ensures seamless integration of pipelines with continuous integration and continuous deployment (CI/CD) tools and workflows.
Supporting and maintaining solutions in production (fixing bugs, make changes as required, maintaining models)
Collaborates with cross-functional teams to integrate machine learning and business logic-based solutions into production systems
Effectively communicates and applies machine learning engineering value, concepts, and strategies in various scenarios with stakeholders
Recruits, hires, and mentors' top talent to build a high-performing MLOps team. Supervises, coaches, and guides direct reports
Uses advanced knowledge of code management principles to follow architectural and governance guidelines
Requirements
5 years of experience required in deploying and managing machine learning pipelines, or related work.
Full English Fluency
Experience in a leadership role within a fast-paced, technology driven environment
Team: Data Scientist, Python Developers, Cross disciplinary (Underwriting, Actuary) 2 Direct Reports