Responsibilities
- Lead the creation and supervision of full-cycle machine learning systems, covering data pipelines, feature storage, model deployment, and performance tracking.
- Shape the long-term vision for AI infrastructure and workflow orchestration.
- Manage the planning, development, and upkeep of the organization's AI and machine learning environment.
- Define and enforce MLOps standards to ensure machine learning systems are reliable, testable, and scalable like core software services.
- Collaborate across disciplines to convert business needs into actionable technical specifications with Product Managers, Data Scientists, and ML Engineers.
- Amplify team effectiveness through high-level system design reviews and mentorship in architecture and performance tuning.
- Design and advance applications powered by large language models, including copilots, search tools, virtual assistants, and autonomous agents, with support for retrieval-augmented generation, external tools, and multi-step reasoning.
- Build comprehensive evaluation systems for generative AI, combining offline testing, live performance data, and human feedback loops.
- Promote effective methods in prompt engineering, agent design, and workflow orchestration to ensure systems perform reliably at scale.
- Implement safeguards for generative AI, including defenses against prompt injection, reduction of false outputs, and adherence to ethical AI principles.
- Create standardized processes for model version control, A/B testing, and automatic rollback to detect and correct model drift.
- Ensure AI architecture follows established industry norms, security requirements, and regulatory standards.
- Identify and execute improvements in workflows to boost platform efficiency and system performance.
Work Arrangement
Hybrid — Canada, Spain, Switzerland, United Kingdom, United States
