Datatonic seeks a Lead Machine Learning Engineer to be the technical authority for ambitious client projects. You will set the technical vision, guide teams, and translate complex business challenges into production-grade AI solutions on Google Cloud. This is a hands-on leadership role where you will architect solutions, lead client discussions, and oversee delivery end-to-end.

What You'll Do

Drive technical strategy from pre-sales to delivery, acting as the lead technical authority in high-stakes engagements.
Partner with the commercial team to architect winning solutions and lead the delivery of enterprise-grade systems.
Architect and implement production-grade solutions, owning the complete technical lifecycle for projects.
Design end-to-end ML architectures on GCP and implement robust MLOps pipelines using Infrastructure-as-Code (Terraform).
Ensure all solutions are optimized for performance, scalability, and security.
Shape technical standards by collaborating with the Head of Delivery to define the technical DNA of the ML practice.
Lead strategic internal initiatives, spearheading the development of internal accelerators and reusable frameworks.
Cultivate engineering talent by formally mentoring and coaching junior and mid-level engineers.

What We're Looking For

7+ years of professional experience in machine learning and software engineering.
At least 2 years in a formal or informal leadership capacity (e.g., tech lead, project lead, or senior mentor).
Proven ability to architect and deploy scalable, production-grade ML solutions on a major cloud platform (GCP is a significant asset).
Hands-on experience with Infrastructure-as-Code tools (e.g., Terraform) and designing for distributed computing.
Deep, hands-on expertise in Python for backend ML systems.
Mastery of software engineering best practices (e.g., clean architecture, robust testing, CI/CD).
Fluent in designing and building REST APIs (e.g., using Flask/FastAPI).
Proficient in SQL for complex data manipulation.
Exceptional ability to communicate complex technical concepts to diverse audiences, from C-level stakeholders to junior engineers.
Excel at leading technical discussions, presenting solutions, and mentoring teammates.

Nice to Have

Prior experience in a client-facing or consulting role.
Professional Google Cloud certifications (e.g., Professional Machine Learning Engineer).
Deep experience with the broader MLOps ecosystem (e.g., Kubeflow, Vertex AI Pipelines, MLflow).
Experience building interactive demos for ML models (e.g., using Streamlit, Gradio).

Technical Stack

Google Cloud Platform (GCP)
Python
Terraform
Flask/FastAPI
SQL
Kubeflow
Vertex AI Pipelines
MLflow
Streamlit
Gradio

Team & Environment

You will collaborate directly with the Head of Delivery and lead and mentor teams of talented engineers. You'll be part of a culture where innovation isn’t just encouraged—it’s embedded in everything we do, working alongside AI enthusiasts and data experts shaping tomorrow.

Benefits & Compensation

Compensation: CAD 175,000 - 200,000
20 days of paid vacation per calendar year
Public Holidays for your Province of Residence
5 Wellness days (sickness, personal time, mental health)
5 Lifestyle days (religious events, volunteer day, sick day)
Matching Group Retirement Savings Plan after 3 months
Competitive Group Insurance plan on Day 1 - individual premium paid 100%
Virtual Medicine and Family Assistance Program - 100% employer-paid
Home office budget - We are 100% remote
CAD $70/month for internet/phone expenses
CAD $1,500 every 3 years for tech accessories and office equipment starting on Day 1
Company-supplied MacBook Pro or Air
CAD $400/year for books, relevant app subscriptions or an e-reader
Opportunities for paid certifications
Opportunities for professional and personal learning through Udemy Business
Regular company off-sites and meetups