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Platform Engineer, Machine Learning

  • Toronto, ON, Canada

Full-time

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Job details

Job type
Full-time
Posting language
English
Posted

Original job description

RAVL is a boutique technology advisory and engineering firm focused on the financial services industry. Everything we do is centered on helping our clients realize measurable ROI from their technology investments.

We’re hiring Senior ML Platform Engineers (Contract) to contribute to building and scaling machine learning platforms across a range of client engagements. This work includes developing ML pipelines, extending platform capabilities, and supporting MLOps workflows in cloud environments.

These roles support both active delivery and upcoming opportunities, offering the chance to work on modern, production-grade ML systems.

What does success look like in this role?

  • Build and extend ML platform components on Databricks
  • Develop ML pipelines for training, deployment, and monitoring
  • Implement reusable workflows aligned with platform architecture
  • Integrate with enterprise tooling and services
  • Support performance, reliability, and observability


Great, do my skills fit?

  • Strong Databricks and Spark experience
  • Experience building ML pipelines and workflows
  • Proficiency in Python
  • Cloud experience (Azure preferred)
  • Familiarity with CI/CD and automation


Nice To Have Skills

  • MLflow or similar tools
  • Enterprise integration experience


Mindset Traits

  • Strong builder and problem solver
  • Detail-oriented and delivery-focused
  • Collaborative


Why join RAVL?

  • Flexible, client-aligned work model - autonomy with accountability, adapting to client delivery needs
  • Variable bonus & RRSP contributions tied to performance and delivery impact
  • 4 weeks paid time off (plus public holidays)
  • Paid professional development days and continuous learning opportunities
  • Comprehensive health & dental coverage, including mental health support
  • Commitment to lifelong learning - continuous improvement through training, mentorship, and certification


Compensation & Hiring Process

We are recruiting for a current opportunity. Final offers are based on experience, skills, and qualifications. As part of our hiring process we may use technology, including AI-based tools, to help summarize and assess applications; these tools assist our team and do not replace human review or decision-making.

Equal Opportunity & Accessibility

RAVL is an equal opportunity employer committed to building a diverse, inclusive, and accessible workplace. We welcome applications from all qualified individuals and provide accommodations throughout the hiring process upon request.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
More about this job

Responsibilities

The role involves building and extending machine learning platform components on Databricks and developing ML pipelines for training, deployment, and monitoring. Additionally, it includes implementing reusable workflows and integrating with enterprise tooling and services.

Requirements

Candidates should have strong experience with Databricks and Spark, as well as proficiency in building ML pipelines and workflows. Familiarity with cloud environments, particularly Azure, and CI/CD practices is also required.

Skills

  • Databricks
  • Spark
  • ML Pipelines
  • Python
  • Cloud
  • CI/CD
  • Automation
  • MLflow
  • Enterprise Integration

Benefits

  • Flexible Work Model
  • Variable Bonus
  • RRSP Contributions
  • Paid Time Off
  • Comprehensive Health Coverage
  • Dental Coverage
  • Mental Health Support

Visa sponsorship

Not detected in the job text

Categories

  • Technology
  • Engineering
  • Consulting
  • Data & Analytics

Keywords

  • Machine Learning
  • ML Pipelines
  • MLOps
  • Cloud
  • Databricks
  • Spark
  • Python
  • CI/CD
  • Automation
  • MLflow
  • Enterprise Integration
  • Performance
  • Reliability
  • Observability
  • Technology Advisory
  • Engineering
  • Financial Services

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