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 up-to-date 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

📌 Director of AI (Toronto)
🏢 Jobtailor
📍 Toronto