AI Engineering Director
- Toronto, ON, Canada
Full-time
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Job details
- Job type
- Full-time
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- Posting language
- English
- Posted
Original job description
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/customersNice 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
More about this job
Responsibilities
Own and drive the company’s AI/ML strategy, leading the design and delivery of scalable AI infrastructure, LLM-powered features, and MLOps pipelines. Build and mentor engineering teams, partner with product, data, and business stakeholders, and guide the development, deployment, and continuous improvement of intelligent agents.
Requirements
The role calls for senior AI engineering leadership, including experience with privacy-preserving machine learning for sensitive HR data, HR system integrations, production LLM optimization, and ML governance and bias auditing. Additional relevant experience includes generative AI products, transformer architectures, fine-tuning, modern data engineering, and scaling teams in high-growth environments.
Skills
- AI/ML Strategy
- AI Infrastructure
- Large Language Models
- MLOps
- Engineering Leadership
- Generative AI
- Privacy-Preserving Machine Learning
- Differential Privacy
- Federated Learning
- HR Systems Integration
- Model Optimization
- Machine Learning Governance
- Compliance
- Bias Auditing
- Data Engineering
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Engineering
- Management & Leadership
- Human Resources
Keywords
- AI/ML Strategy
- Scalable AI Infrastructure
- Large Language Models
- MLOps Pipelines
- Generative AI
- Intelligent Agents
- Privacy-Preserving Machine Learning
- Differential Privacy
- Federated Learning
- Applicant Tracking Systems
- HRIS
- Recruiting CRMs
- Hiring Workflows
- Model Quantization
- ONNX
- Triton Inference
- Vector Databases
- Cloud Cost Management
- ML Governance
- GDPR
- PIPEDA
- EEOC
- Anti-Bias Auditing
- Model Risk Frameworks
- Transformer Architectures
- Fine-Tuning
- Data Engineering
- Modern Data Stack
- Talent Acquisition