Lead & Senior Machine Learning Engineer - MLOps & Platform Engineering

Myticas Consulting; Toronto; On-site; CAD 90,000 - 120,000; Full time

Job summary

A technology consulting firm is looking for a highly experienced Machine Learning Engineer in Toronto. This role is hands-on and technical, requiring architecture and delivery of ML solutions. Candidates must have a solid background in production-grade ML engineering and at least 5 years of experience. Strong communication skills and collaboration with stakeholders are essential. Competitive package offered.

Qualifications

• 5+ years in software/data/DevOps engineering roles, with 3+ years in ML engineering or MLOps.
• Strong academic foundation in ML algorithms and data engineering.
• Advanced Python coding capabilities.

Responsibilities

• Architect and deliver ML pipelines end-to-end using Databricks and MLflow.
• Create and maintain large-scale data processing workflows within Databricks.
• Automate and manage the full ML lifecycle from model training to monitoring.

Skills

Machine Learning

MLOps

Python

Databricks

CI/CD

Azure

Data Engineering

Education

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field

Tools

Databricks

Azure ML

TensorFlow

PyTorch

scikit-learn

Job description

Myticas Consulting is seeking a highly experienced Machine Learning Engineer to join a fast-paced, client-facing environment focused on delivering scalable ML solutions. This role is hands-on and deeply technical, involving the modernization and migration of mature ML models within a complex production ecosystem. You’ll be leading architectural efforts, implementing robust MLOps practices, and supporting the integration of ML applications into SaaS platforms. Strong communication skills are essential, as the position requires regular collaboration with stakeholders and mentoring of junior team members.
This opportunity is open exclusively to candidates located in Canada with a solid background in production-grade ML engineering—not recent graduates.

Role/Responsibilities
Platform Engineering & MLOps

• Architect and deliver ML pipelines end-to-end using tools like Databricks and MLflow.
• Create and maintain large-scale data processing and feature engineering workflows within Databricks.
• Automate and manage the full ML lifecycle: model training, testing, deployment, and monitoring.
• Introduce CI/CD automation with platforms such as GitHub Actions or Azure DevOps.
• Ensure high availability, security, and scalability across ML workflows.
• Migrate models to SaaS environments and support cross-platform integrations.

Leadership & Collaboration

• Act as a technical leader on ML-focused client engagements, driving delivery and alignment with business outcomes.
• Translate strategic objectives into practical ML implementations and delivery roadmaps.
• Partner with data scientists, engineers, and product stakeholders to embed ML models into production environments.
• Mentor junior team members and champion ML engineering best practices across engagements.

ML Application Engineering

• Convert experimental models into scalable, production-ready solutions.
• Apply and optimize ML algorithms across a mix of structured and unstructured datasets.
• Measure model performance, run experiments, and iterate for improvement.
• Maintain documentation and contribute to internal knowledge-sharing frameworks.
• Assist with the scale-up and migration of 25+ models in a production environment already housing over 100 ML models.

Experience
Mandatory

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a closely related technical field.
• Strong academic and practical foundation in ML algorithms, distributed computing, and data engineering.

• 5+ years in software/data/DevOps engineering roles, with 3+ years specifically in ML engineering or MLOps.
• Demonstrated experience launching and managing machine learning solutions in production.
• Expertise with Azure-based ecosystems (Azure ML, Data Lake, AKS, Azure DevOps).
• Databricks proficiency is a must—especially working with Delta Lake, MLflow, and Apache Spark.
• Advanced Python coding capabilities and experience with frameworks like TensorFlow, PyTorch, and scikit-learn.
• Familiarity with CI/CD tools and infrastructure-as-code frameworks (e.g., Terraform).
• Strong communication skills and a collaborative mindset.
• Must be legally authorized to work in Canada and currently residing in the country.

Nice to Have

• Certifications in Azure or Databricks (e.g., Databricks Certified Professional, Microsoft Certified: Azure Solution Architect).
• Exposure to LLMs and frameworks like Transformers, TRL, or DeepSpeed.
• Background in migrating ML models to SaaS platforms.
• Prior experience in a consulting role, with a track record of effective client engagement.