- Lead technical strategy, architecture, and delivery of AI applications from discovery through production and scale - Work with client executives, product leaders, architects, and engineering teams to identify AI opportunities and create technical roadmaps - Design AI applications combining models, enterprise data, APIs, software components, user experiences, and human workflows - Guide agentic workflows, RAG, enterprise search, predictive models, and AI capabilities embedded in digital products - Decide when to use deterministic software, machine learning, LLMs, human review, or combinations - Establish evaluation-driven development with test datasets, error analysis, deterministic checks, model-based evaluation, and business outcome measurement - Ensure production reliability, observability, scalability, latency, maintainability, security, and cost requirements - Guide CI/CD, model and prompt versioning, monitoring, tracing, regression testing, and optimization - Provide hands‑on technical leadership through prototyping, architecture reviews, code reviews, troubleshooting, and delivery oversight - Use AI-assisted development and coding agents with verification, security, and human oversight - Support proposals, discovery workshops, solution design, estimates, and executive presentations - Develop reusable AI engineering patterns, reference architectures, accelerators, and delivery standards - Contribute to hiring, technical mentorship, and growth of APPLY's AI capability Requirements - 10+ years of experience across software engineering, data engineering, machine learning, or related technology disciplines,



including significant experience leading AI or ML solutions - Experience designing, building, and operating AI or machine learning applications in production - Strong foundation in system design, APIs, data architecture, testing, security, cloud infrastructure, and production operations - Hands‑on proficiency in Python and modern application, data, and AI engineering frameworks - Understanding of LLMs, RAG, context engineering, agentic workflows, tool use, structured outputs, and model evaluation - Experience grounding AI systems in enterprise data, including structured data, documents, semantic models, vector stores, or knowledge graphs - Experience establishing evaluation and error‑analysis practices for probabilistic-output systems - Ability to balance model quality, reliability, latency, cost, security, and user experience - Experience with cloud‑native architecture and production deployment on GCP, AWS, or Azure - Familiarity with containers, CI/CD, observability, and MLOps or LLMOps - Success in consulting, qualified‑services, or complex client‑facing environments - Ability to work across executive conversations, product decisions, architecture discussions, and detailed technical problem‑solving - Excellent communication skills with technical and non‑technical audiences - Degree in computer science, software engineering,



artificial intelligence, data science, or related field—or equivalent professional experience - Preferred: experience in regulated, privacy‑sensitive, or large‑scale enterprise environments; model fine‑tuning; open‑source models; multimodal AI; voice agents; computer‑use agents; enterprise AI security controls; internal AI platforms; organizational AI strategy;

certifications or delivery experience with GCP, Snowflake, Databricks, or comparable platforms Core Competencies Demonstrates expertise in leading the technical strategy and architecture of AI applications, with a strong foundation in system design, data architecture, and production operations. Proficient in Python and modern AI engineering frameworks, with a focus on ensuring production reliability, scalability, and security. Highest-signal resume keywords - AI Application Development - Machine Learning Solutions Leadership - Cloud Infrastructure Deployment (GCP, AWS, Azure) - System Design and Data Architecture - CI/CD and MLOps Practices ATS Optimization Keywords Hard Skills - Python - AI Engineering Frameworks - Machine Learning - Data Architecture - System Design - Model Evaluation - Error Analysis - Production Operations - API Development - Cloud‑Native Architecture Soft Skills - Excellent Communication Skills - Client‑Facing Experience - Technical Mentorship Industry Keywords - AI Applications - Machine Learning - Enterprise Data - Regulated Environments - Privacy‑Sensitive Environments - Organizational AI Strategy Tools & Technologies - GCP - AWS - Azure - Containers - CI/CD Tools - MLOps - LLMOps

📌 Director of AI (Toronto)
🏢 Jobtailor
📍 Toronto