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
Work Mode
This is a local-country, 100% remote position for candidates located in Canada.
Datatonic is an equal opportunity employer.



