Responsibilities
- Collaborate with product and engineering leaders to align business goals with a long-term technical vision for the machine learning platform.
- Create standardized reference architectures and engineering guidelines for scalable data and ML workflows, covering training, evaluation, deployment, and serving.
- Lead the development of company-wide MLOps practices, including model lifecycle management, CI/CD pipelines, feature stores, and experiment tracking.
- Guide strategic decisions on building or acquiring core platform components based on technical and business requirements.
- Design systems for production reliability, observability, and performance of ML models, including monitoring, alerts, and self-healing mechanisms.
- Develop and enforce security standards for the ML platform, including access controls, authentication, audit trails, and compliance oversight.
- Define scalable and secure integration patterns to connect the ML platform with existing data sources, APIs, and enterprise systems efficiently.
Work Arrangement
Hybrid
Responsibilities
- Partner with product and engineering leadership to translate business and product objectives into a multi-quarter technical strategy and roadmap for the ML platform.
- Define the reference architectures and standards for scalable data and ML pipelines spanning model training, evaluation, deployment, and serving that engineering teams across the organization build upon.
- Set the direction and best practices for MLOps across the company — including CI/CD for models, model registry, feature stores, and experiment tracking — and drive build-vs-buy decisions for core platform components.
- Establish the practices and architecture for reliability, observability, and performance of ML systems in production, including monitoring, alerting, and automated remediation.
- Establish the security architecture for the ML platform, including authentication, role-based access control, audit logging, and compliance monitoring, and ensure adoption across teams.
- Define secure, cost-efficient integration and infrastructure patterns for connecting the platform with existing systems, APIs, and data sources at scale.
- Provide technical leadership and mentorship across engineering teams, guiding senior engineers and influencing the org-wide technical roadmap for ML infrastructure.
