CONFIDENTIAL Scovai, a trailblazer in the Talent Acquisition & Recruiting sector, is redefining how organizations build and manage their workforce through an intelligent, AI-powered talent platform that spans the entire employee lifecycle. As part of a confidential search, Scovai is seeking an exceptional AI Engineering Director to join their Toronto-based team in a hybrid capacity. This is a rare opportunity to shape the technical vision of a company at the forefront of transparent, auditable AI - where algorithmic precision meets human judgment to transform the future of work.
As AI Engineering Director, you will own and drive Scovai's AI/ML strategy end-to-end, leading the design and delivery of scalable AI infrastructure, LLM-powered features, and MLOps pipelines that underpin the platform's core capabilities. You will build, mentor, and scale high-performing engineering teams, partnering closely with product, data, and business stakeholders to translate ambitious roadmaps into production-grade AI systems. Your technical leadership will directly influence how intelligent agents are developed, deployed, and continuously improved across the platform.
This role is as exciting as it is impactful: you will operate at the intersection of cutting-edge generative AI research and real-world product delivery, with the autonomy to make foundational architectural decisions and the resources to execute them. If you thrive in high-growth environments, are passionate about responsible AI, and want to leave a lasting mark on an industry being fundamentally reimagined, Scovai offers the ideal stage.
Hands-on experience implementing privacy-preserving ML (differential privacy, federated learning, or secure multi-party computation) for sensitive talent/HR data Proven integrations with HR systems (ATS, HRIS, recruiting CRMs) and deep understanding of hiring workflows and data models Expertise in production LLM performance and cost optimization (model quantization, ONNX/Triton inference, vector DBs, and cloud cost management) Practical experience with ML governance, compliance, and anti-bias auditing in hiring contexts (GDPR/PIPEDA, EEOC considerations, model risk frameworks) Contributions to the ML/AI community (open-source projects, publications, or participation in standards bodies) or experience evangelizing technical strategy to external partners/customers
Nice to have
- Hands-on experience building generative AI products at scale
- Deep knowledge of transformer architectures and fine-tuning methodologies
- Background in data engineering and modern data stack design
- Proven track record scaling AI teams in startup or high-growth environments
- Open-source contributions to AI/ML frameworks or tooling
