Senior Director Of Ai Solutions (Toronto)

Requirements

• 12–15+ years of experience in AI/ML, data science, or distributed systems engineering.
• 8+ years leading ML or AI engineering teams in platform or infrastructure environments.
• Proven track record delivering production‑grade AI systems at enterprise scale.
• Experience partnering with Sales, Marketing, or Customer Success organizations to drive business outcomes.
• Deep hands‑on experience with generative AI including LLMs, prompt engineering, and fine‑tuning. Strong experience with retrieval‑augmented generation architectures and agentic AI systems.
• Proficiency with ML frameworks such as PyTorch or TensorFlow.
• Experience with cloud platforms including AWS, GCP, or Azure, and modern data stacks.
• Strong understanding of distributed systems, CI/CD, experimentation frameworks, and observability.
• Ability to operate as both a technical leader and business partner.
• Strong executive communication and stakeholder management skills.
• Experience influencing senior stakeholders and driving alignment across highly matrixed organizations.
• Ability to translate complex technical systems into explicit business value.
• Experience with building AI solutions for Enterprise and driving value through revenue growth like Recent Logo’s, Recommendations for Cross‑sell‑Upsell.
• (Desirable) Experience with Salesforce or enterprise CRM ecosystems.
• Experience building internal AI platforms or developer tooling.
• Background in SaaS or enterprise software environments.
• Advanced degree in Computer Science, Machine Learning, or a related field.

What the job involves

• Lead End‑to‑End GenAI and ML System Delivery

  Design and deploy generative AI and machine learning solutions embedded in CRM and customer workflows. Build systems including LLM‑powered copilots, agentic workflows, retrieval‑augmented generation pipelines, and predictive models. Own the full lifecycle from data sourcing and model development to evaluation, deployment, and monitoring.

• Drive Hands‑On ML Engineering Excellence

  Architect and guide implementation of model training and fine‑tuning pipelines using frameworks such as PyTorch, TensorFlow, and Hugging Face. Build real‑time and batch inference systems, embedding pipelines, and vector database integrations. Establish best practices for CI/CD, experimentation, model evaluation, and observability across AI systems.

• Scale Agentic AI Systems

  Design and deploy agent‑based systems capable of multi‑step reasoning, tool usage, and workflow orchestration across enterprise platforms. Establish reusable patterns for prompt management, tool integration, policy enforcement, and agent lifecycle management.

• Drive Revenue Impact Through AI

  Partner with Sales and go‑to‑market leadership to embed AI into deal strategy and customer engagements. Contribute directly to pipeline growth, deal velocity, and account expansion. Translate AI capabilities into measurable outcomes including conversion improvement, productivity gains, and customer retention.

• Lead Cross‑Functional Execution

  Collaborate across Engineering, Data, Product, Sales, and Customer Success to deliver integrated AI solutions. Translate complex business problems into scalable technical architectures and ensure alignment across systems, data, and workflows.

• Build and Lead High‑Performing Teams

  Lead and grow teams of ML engineers, ML scientists, and AI architects. Establish a high bar for engineering quality, execution, and technical depth. Build scalable development practices and delivery models for enterprise AI.