Who we are
We’re a thriving and agile tech development firm. People are at the center of everything we do. Simply put, we care. Our team personally cares about our clients and the world around them, and we care about our team’s life-long dreams, aspirations, and career development.
We strongly believe in the power of community. This is why we seek opportunities to build meaningful relationships with everyone around us.
We celebrate diversity in all its forms. Backgrounds, personalities, career paths, languages... you name it. We actively innovate, learn, and share stories around the topic. We want everyone to feel welcomed and included in all we do.
We like having fun and enjoying ourselves. We wake up every day inspired to build a more efficient and enjoyable world.
As a Senior Software Developer with strong Data Engineering and MLOps experience, you design, build, and deliver robust data and machine learning solutions for our clients. You combine strong software engineering habits with hands-on experience in data pipelines, cloud environments, automation, model deployment, monitoring, and production-ready ML systems.
You’re comfortable moving between backend development, data engineering, ML lifecycle practices, infrastructure, CI/CD, observability, and technical leadership. You collaborate closely with technical and non-technical teammates, mentor others, and help teams make sound technical decisions as we deliver enterprise-grade AI and data solutions.
The technologies below are a reference point for our stack. Above all, we hire for strong fundamentals, judgment, ownership, and growth potential.
- 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
- 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
- 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
- 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
- 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...
1. 20 min - Initial introductory call with our technical team
2. 90 min - In-person interview at our office.
3. Offer presentation
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. 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.
