Description
As a Lead Machine Learning Engineer at Faculty, you will set the technical direction for complex AI/ML projects, ensuring models perform at scale and in production over time by balancing technical trade-offs and guiding team priorities.
You will lead the delivery of large-scale AI-powered platforms in high-risk environments while defining project roadmaps across multiple complex workstreams.
This is an ambitious, entrepreneurial leadership role where you will act as a trusted technical expert, defending your architectural rationale to senior stakeholders to ensure we deliver high-quality, high-value outputs.
Responsibilities:
- Designing, implementing, and maintaining reliable, scalable ML systems while justifying key architectural decisions for production environments.
- Driving the development of shared libraries and infrastructure for model deployment, lifecycle management, and CI/CD pipelines.
- Leading model integration with infrastructure by creating APIs and services that enable scalable AI functionality in applications.
- Overseeing the delivery of multiple complex workstreams and defining project problems in high-risk environments.
- Ensuring reliable model performance by defining testing frameworks and model versioning systems for senior engineers to implement.
- Managing and coaching multiple individuals, setting team-wide development goals to improve technical depth and client delivery.
- Executing proactive recommendations for adopting new technologies and AI frameworks to maintain Faculty's competitive market position.
Requirements:
- You are an expert at defining technical roadmaps and managing project priorities to deliver high-stakes outcomes within high-growth environments.
- You possess mastery of cloud-native ecosystems and orchestration tools like Kubernetes to automate complex model lifecycles and robust CI/CD pipelines.
- You have a proven ability to design large-scale, AI-powered platforms and provide the technical justification for critical architectural decisions in high-risk environments.
- You bring expert-level experience in operationalising models within frameworks like TensorFlow or PyTorch to solve complex, high-impact business challenges.
- You define the engineering standards and architecture patterns for agentic systems, ensuring teams build to a consistent, production-grade bar.
- You demonstrate an exceptional ability to align multi-disciplinary technical teams with broader business objectives and evolving customer needs.