ARUP Laboratories is looking to establish the long-term strategic direction for the organization's ML infrastructure and its technical implementation to enable the organization to leverage AI at scale.
Requirements
- Deep understanding of multiple ML frameworks and their serving requirements.
- Expertise in distributed systems, big data solutions, and complex system design.
- Expertise in cloud platforms (AWS, GCP, MS Azure)
- Proven ability to lead and architect enterprise-scale MLOps systems.
- Proficiency in cloud computing architecture and design patterns.
- Ability to read and gain insights from scientific literature to inform technical decisions.
Responsibilities
- Designs and improves systems for training, evaluating, deploying, and monitoring machine learning models in production across the organization.
- Stays current with modern ML Ops best practices through review of technical literature, scientific articles, whitepapers, conferences, and related material.
- Sets technical standards and best practices for production ML systems.
- Acts as a technical authority and mentor for MLOps, ML, and Data Engineering teams.
- Works with leadership to define the MLOps technology roadmap and long-term strategy and guides teams to work toward those long-term goals.
- Communicate with data science, engineering, IT, and medical director teams to understand requirements, constraints, and operational workflows.
- Responsible for all aspects of the model development lifecycle (training, deployment, monitoring, re-training, etc.) at an architectural level.
Other
- Bachelor's degree in related field and 10 years of experience in Machine Learning / Data Engineering
- Five years of experience leading ML Ops / Data Engineering teams
- Master’s degree in related field
- Exceptional leadership and communication skills, with a track record of mentoring and developing top talent.
- Stooping: Bending body downward and forward by bending spine at the waist.
