Senior Software Developer, Data & MLOps

Team: Software Development

Location: Montreal (EN)

Commitment: Full-time

Workplace Type: hybrid

Role overview

Your key responsibilities

• Design, build, and deliver scalable software, data, and machine learning solutions for client projects
• Identify solutions to cross-functional problems using your software development, data engineering, and MLOps experience
• Design, plan, and implement data pipelines, ML workflows, and supporting cloud or on-premise infrastructure
• Develop end-to-end solutions aligned with specifications and documentation
• Build and improve CI/CD pipelines, data pipelines, model deployment workflows, and automation practices
• Contribute to containerized, virtualized, and cloud-native environments that support data and ML workloads
• Support modernization initiatives by improving architecture, testing, deployment, observability, data quality, and maintainability
• Define, document, and communicate non-functional requirements such as performance, reliability, security, scalability, and maintainability
• Support ML lifecycle practices such as experiment tracking, model versioning, validation, deployment, promotion, rollback, and monitoring
• Coach colleagues on software development, data engineering, MLOps, and delivery best practices
• Take initiative, own deliverables end-to-end, and manage priorities effectively
• Uphold and strengthen software development guidelines and quality standards
• Research, test, and implement new techniques, tools, and technologies
• Advise clients on technical direction, trade-offs, architecture, data platforms, and ML solution design

The ideal candidate

• 5+ years of software development experience, including recent hands-on experience with data engineering, MLOps, or production ML systems
• Bachelor’s degree, college degree, certification in a software-related field, or equivalent experience
• Intermediate or conversational French at a minimum
• Strong backend development experience
• Strong technical judgment and ability to make pragmatic architectural decisions
• Experience building or supporting data pipelines, data platforms, or ML deployment workflows
• Experience collaborating directly with clients or stakeholders
• Ability to mentor teammates and help raise the quality of technical delivery
• Comfortable working in ambiguous environments and bringing structure to complex problems

You should be proficient with

• At least one major cloud platform such as AWS, Azure, or Google Cloud
• At least one major server-side programming language such as Python, Java, Node.js/TypeScript, Go, C#, or similar
• At least one major data engineering platform such as Databricks, Snowflake, BigQuery, Microsoft Fabric, or similar
• Backend development, API design, and distributed systems
• Data pipeline orchestration, version control, data validation, feature pipelines, or feature stores
• ML lifecycle practices such as experiment tracking, model versioning, validation, deployment, promotion, rollback, and monitoring
• CI/CD pipelines and deployment automation
• Infrastructure as code and provisioning tools such as Terraform, CDK, CloudFormation, Bicep, Ansible, or similar
• Virtualization and containerization, ideally in a Linux-based ecosystem
• Docker and orchestration tools such as Kubernetes or Docker Compose
• Microservices, serverless systems, or cloud-native architectures
• Monitoring and observability tooling and services
• Testing practices such as unit, integration, functional, end-to-end, or load testing
• Modern development methodologies such as Agile, Scrum, XP, Kanban, Shape Up, etc.
• Cloud cost awareness, calculation, and optimization

It’s a plus if you have experience with

• A modern client-side framework/library such as React, Angular, Svelte, Vue, Remix, or similar
• Full-stack web development
• LLMOps, RAG systems, vector databases, or GenAI application deployment
• Edge computing, IoT, robotics, industrial systems, or hardware-adjacent software
• Simulation environments or developer tooling that improves delivery speed
• In-memory object storage, caching, and queue systems
• Event-driven architecture or messaging systems such as MQTT, Kafka, RabbitMQ, Redis, or similar
• Hexagonal architecture
• Domain-driven design
• High-availability systems
• Technical leadership in client-facing projects
• Application security, networking, identity, or compliance considerations

What we offer

• Competitive Salary and contribution to your pension plan (RRSP)
• Flexible hours of work and choose how you work 
• Work from anywhere up to 8 weeks
• Paid sabbatical
• Wellness and productivity spending account
• Parental program
• Activities
• Training
• And more...