Senior Software Developer, Data & MLOps

OSEDEA•Montreal, QC

6d•Remote

About The Position

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.

Requirements

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

Nice To Haves

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

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