Lead Machine Learning Engineer

In this role, you will act as a technical leader driving the design and delivery of advanced machine learning solutions for enterprise clients across multiple industries. You will shape end‑to‑end AI architectures on cloud platforms, translating complex business problems into scalable, production‑ready ML systems. The position combines hands‑on engineering with client‑facing consulting, requiring both deep technical expertise and strong communication skills.

You will guide project teams through the full lifecycle of delivery, from solution design and pre‑sales engagement to deployment and optimization. A key part of your impact will be defining technical standards, mentoring engineers, and building reusable frameworks that elevate delivery quality. This is a high‑ownership leadership role at the forefront of applied AI and cloud innovation.

Accountabilities

Lead the technical strategy across pre‑sales and delivery phases, designing ML solutions aligned with client business needs

Architect and implement end‑to‑end machine learning systems on cloud platforms, particularly GCP, ensuring scalability and performance

Build and maintain production‑grade MLOps pipelines using tools such as Terraform and Infrastructure‑as‑Code practices

Develop, deploy, and optimize machine learning models, including GenAI, recommendation systems, and predictive analytics solutions

Define and evolve engineering best practices, architectural standards, and delivery frameworks across the ML practice

Lead client‑facing technical discussions, translating complex AI concepts into clear, business‑oriented solutions

Mentor and coach junior and mid‑level engineers through code reviews, technical guidance, and career development support

Drive internal initiatives such as reusable accelerators, frameworks, and tooling to improve delivery efficiency

Ensure high standards of software engineering quality, including testing, CI/CD, and system reliability

Collaborate with cross‑functional teams to ensure successful deployment and adoption of ML solutions

Requirements

7+ years of experience in machine learning engineering, data science, or software engineering roles

At least 2 years of leadership experience in a tech lead, project lead, or mentoring capacity

Strong experience designing and deploying production‑grade ML systems on cloud platforms (GCP strongly preferred)

Proficiency in Python for backend ML development, including clean architecture and software engineering best practices

Strong experience with Infrastructure‑as‑Code tools such as Terraform and MLOps frameworks

Solid understanding of REST APIs development using frameworks like Flask or FastAPI

Strong SQL skills for complex data manipulation and analysis

Excellent communication skills with ability to engage both technical and executive stakeholders

Proven ability to mentor engineers and lead technical discussions in consulting or client‑facing environments

Experience with MLOps tools (e.g., Kubeflow, Vertex AI Pipelines, MLflow) is a strong plus

Exposure to GenAI systems, distributed computing, or cloud certifications is a bonus

Benefits

Competitive salary ranging from CAD $175,000 to $200,000

Fully remote work across Canada with home office support

20 days of annual paid vacation plus public holidays

Additional wellness, lifestyle, and personal days for flexibility and balance

Comprehensive employer‑paid health, dental, and virtual medical benefits

Retirement savings plan with employer matching after 3 months

Paid certifications and access to professional learning platforms (e.g., Udemy Business)

Annual budget for internet, phone, books, and learning resources

Company‑provided Mac Book and equipment

Regular off‑sites, team events, and collaboration opportunities

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