- 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 contemporary 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, professional‑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

#J-18808-Ljbffr

📌 Director of AI (Toronto)
🏢 Jobtailor
📍 Toronto