Machine Learning Jobs in Montreal, QC (NOW HIRING)

Job Description: Application deadline: September 11, 2026 12:00PM ET Join the EDC Team! At EDC, we support Canadian businesses to succeed globally. We provide the financial tools and expertise they need to explore new markets, reduce risks, all towards the goal of making Canada and the world better through trade. Position: AI Machine Learning Engineer, Level 17 or 18 Employment Type: Permanent Compensation Details: Machine Learning Engineer 17: Salaries typically range from $84,698 to $112,931 annually, based on qualifications and experiences, plus a performance-based incentive. Machine Learning Engineer 18: Salaries typically range from $96,557 to $128,743 annually, based on qualifications and experiences, plus a performance-based incentive. Location: Export Development Canada operates under a hybrid work model, with employees currently required to work from the office two days per week. Effective September 2026, this requirement will increase to three days per week and will be implemented gradually through a phased approach based on employee location. (subject to change). This role can be performed from EDC’s headquarters in Ottawa or from one of our Community Hubs located in Toronto or Montreal. Relocation assistance is available for candidates who meet the eligibility criteria. Internal Employees, please consult the ServiceNow article entitled Internal movements – what you need to know. About EDC: At Export Development Canada (EDC), we empower Canadian businesses to succeed globally. As a financial Crown corporation, we offer innovative financial solutions and expert insights to help businesses explore new markets, mitigate risks, and achieve growth.Why Join EDC? Comprehensive Benefits: EDC offers a competitive compensation & benefits package, work-life balance, & the opportunity to help make Canada and the world better through trade. Work-Life Balance: EDC offers a competitive compensation package & work-life balance. We have hybrid work options, 3 to 4 weeks paid vacation, a corporate closure period, summer early Friday’s & no meeting Fridays. Professional Development: Take advantage of our continuous learning opportunities, including training programs, workshops and language training. Inclusive Culture: Be part of a diverse and inclusive workplace that champions employment equity & values diversity of ideas, strengths, & backgrounds to succeed. Wellness Programs: Access to wellness initiatives, mental health support, and fitness programs to keep you healthy and happy. Community Engagement: Participate in volunteer opportunities and give back to the community through our various social responsibility programs. Team Overview: The Digital & Technology Solutions (DTS) group under the leadership of the Chief Information Officer was established in 2023 with the mission of empowering our customers and colleagues to take on the world, by seamlessly delivering secure and reliable digital experiences. Digital & Technology Solutions has set out to achieve the following objectives for EDC: Define, execute, and sustain the integrated technology target state, target data model and technology operations required to enable EDC’s 2030 business transformation. Establish and manage the rolling 3 Year Digital Roadmap that sequences the technology outcomes required to achieve the technology target state and facilitate its execution across all domains in the organization. Keep pace with industry trends and emerging technologies, ensuring EDC has access to the digital technology tools it needs to stay relevant in the market and grow Canadian global trade. Lead and ensure integrated digital, data, infrastructure, and cybersecurity implementations to create excellent customer, user, and employee experiences. Why Join the AI Centre of Enablement? Help shape how AI is responsibly scaled across EDC through enterprise-grade AI and machine learning solutions. Work on production AI products with real business impact, not experimental projects. Influence AI strategy, governance, and delivery at a national institution. Collaborate with business SMEs, data scientists, architects, and platform teams to move AI solutions from experimentation to production. Work across AI/ML engineering, MLOps, cloud platforms, DevOps, and Responsible AI practices. Contribute to model deployment, CI/CD automation, observability, monitoring, and governance in a modern AI environment. If selected for Level 17, the role builds, deploys, and supports production AI and machine learning solutions while working closely with senior engineers and architects. This role offers meaningful production responsibility and the opportunity to develop expertise across the full AI lifecycle, with a clear growth path toward the ownership, leadership, and strategic scope expected at Level 18. If selected for Level 18, the role takes ownership of the end-to-end AI lifecycle, from production engineering and platform operations to governance, scalability, and ongoing optimization. As a senior engineer, you will help shape enterprise AI standards, lead the transition to product-based AI delivery, and drive the reliability, security, and compliance of production AI systems while influencing how AI is delivered across EDC. How do we Build AI at EDC? At EDC, AI solutions are built and operated using standardized, enterprise-grade engineering practices designed to ensure reliability, security, scalability, and responsible use. All production AI systems follow governed design patterns, rigorous evaluation and observability requirements, documented architecture decisions, secure-by-design data handling practices, and structured promotion processes for models and prompts. While these standards apply at both levels, Level 18 engineers are expected to help shape and strengthen these practices, whereas Level 17 engineers apply them under guidance while building toward greater ownership and accountability. What you will be doing: Design and maintain reusable ML assets, including feature pipelines, shared components, deployment patterns, and evaluation frameworks to strengthen organizational AI maturity. Collaborate with data scientists, architects, platform, and security teams to transition models from research to scalable, reliable production services. Additional responsibilities - Level 17: Contribute to the engineering and deployment of production-grade ML and GenAI solutions, including batch and real-time inference patterns, using the same standardized enterprise deployment models. Support the AI system lifecycle (MLOps / LLMOps / AgentOps), including versioning, monitoring, and retraining support, escalating scaling, rollback, and retirement decisions to senior engineers where they carry significant risk. Support GenAI applications (RAG-based and agentic patterns), applying evaluation and guardrail patterns established by senior engineers, and contributing to their improvement. Apply security, governance, and Responsible AI controls by design, working within platform standards, Protected B requirements, auditability, and risk-based controls set by the team. Contribute to CI/CD pipelines, Infrastructure as Code, and standardized environment promotion across experimentation, staging, and production. Monitor AI system health and diagnose issues, including performance degradation, data and model drift, and cost anomalies, escalating and supporting remediation of production incidents within defined SLAs. Build basic machine learning algorithms and support the creation of more complex algorithms that identify patterns in structured data, partnering with business stakeholders to ensure data is collected in accordance with model standards. Provide fault isolation and initial resolution for complex challenges, working within diagnostic approaches designed by senior engineers; produce multiple concepts and prototypes in support of new AI product and service ideas. Analyze specific, well-defined problems and issues to identify and evaluate the best available technical solution. Additional responsibilities - Level 18: Engineer and deploy production-grade ML and GenAI solutions, including batch, real-time, and event-driven inference patterns, using standardized enterprise deployment models, primarily in Databricks platform and within Azure cloud ecosystem. Own the full AI system lifecycle (MLOps / LLMOps / AgentOps), including versioning, monitoring, retraining, scaling, rollback, and retirement of production models. Operationalize GenAI applications (RAG-based and agentic patterns), embedding evaluation, guardrails, and cost awareness as core design principles. Embed security, governance, and Responsible AI controls by design, ensuring compliance with platform standards, Protected B requirements, auditability, and risk-based controls. Automate AI delivery and operations through CI/CD pipelines, Infrastructure as Code, and standardized environment promotion across experimentation, staging, and production. Monitor, diagnose, and remediate AI system health, including performance degradation, data and model drift, bias indicators, cost anomalies, and production incidents within defined SLAs. Build and validate predictive, descriptive, and behavioural models that link directly to business performance indicators, working with business SMEs to translate insight into actionable recommendation - not just a shipped model. Create complex algorithms that identify patterns in structured data through supervised and unsupervised learning, and manage data preparation in close collaboration with business stakeholders and internal clients. Apply strong engineering judgment to balance performance, scalability, cost, security, and risk in enterprise AI systems operating at scale. What we are looking for: University degree in Computer Science, Engineering, Mathematics, Data Science, or a related technical discipline. Level 17: Minimum 5 years of experience in AI and ML engineering roles delivering production systems in enterprise environments. Minimum 5 years of experience designing or contributing to large-scale data platforms, supporting batch and real-time workloads, primarily in Databricks platform and within Azure cloud ecosystem. Minimum 3 years of hands-on experience building and operating ML or AI systems in production, including monitoring, retraining, and incident response. Minimum 3 years of experience working with cloud platforms including deployment, automation, networking, and security services via Azure cloud ecosystem, with focus on Databricks data operations. Minimum 3 years of experience implementing CI/CD pipelines and Infrastructure as Code for ML/AI workloads. Minimum 3 years of experience developing production-grade code using Python and data-centric languages (e.g., SQL, Java, Scala). Minimum 3 years of experience operating within formal IT service management and Agile delivery environments, including incident/change management and cross-functional delivery. Minimum of 1 year of applied experience with GenAI or MLOps concepts, including LLMs, RAG-based architectures, or agentic/workflow-oriented patterns in a production context. Level 18: Minimum 7 years of experience in AI and ML engineering roles delivering production systems in enterprise environments. Minimum 7 years of experience designing or contributing to large-scale data platforms, supporting batch and real-time workloads, primarily in Databricks platform and within Azure cloud ecosystem. Minimum 7 years of hands-on experience building and operating ML or AI systems in production, including monitoring, retraining, and incident response. Minimum 7 years of experience working with cloud platforms including deployment, automation, networking, and security services via Azure cloud ecosystem, with focus on Databricks data operations. Minimum 7 years of experience implementing CI/CD pipelines and Infrastructure as Code for ML/AI workloads. Minimum 5 years of experience developing production-grade code using Python and data-centric languages (e.g., SQL, Java, Scala). Minimum 7 years of experience operating within formal IT service management and Agile delivery environments, including incident/change management and cross-functional delivery. Minimum 3 years of applied experience with GenAI or MLOps concepts, including LLMs, RAG-based architectures, or agentic/workflow-oriented patterns in a production context. What will make you stand out: Experience deploying containerized AI workloads and operating scalable cloud-based infrastructure. Demonstrated ability to collaborate across multidisciplinary teams, communicate complex technical concepts clearly, and operate effectively in complex technical environments. Familiarity with industry AI security, governance, and risk frameworks and how they are applied, including concepts aligned to the Databricks AI Security Framework (DASF) and recognized standards and guidance such as: MITRE ATLAS (AI threat landscape) & MITRE ATT OWASP LLM Top 10 (2025) and OWASP ML Top 10 (v0.3); ISO/IEC 42001:2023 (AI Management Systems), ISO/IEC 27001:2022 (Information Security); NIST SP 800-53 Rev. 5, HITRUST, ENISA Securing ML Algorithms, and the EU AI Act; CAN/DGSI 101:2025 (Ethical Design and Use of AI by Small & Medium Organizations). Bilingual in both official languages (English and French). Level 17: Degree with a strong analytical or technical concentration — e.g., a specialization in Machine Learning, Statistics, or Applied AI within a broader Computer Science or Engineering program. Relevant industry certifications at the Associate tier, such as Databricks Data Engineer Associate, Databricks GenAI Engineer Associate, Azure AI Engineer Associate, Azure DevOps Engineer Expert, or Agile certifications (e.g., DASM). Hands-on exposure to the enterprise ML/AI Databricks Platform and/or Azure AI Foundry, including Git-based development workflows. Contribution to production or near-production GenAI solutions in Databricks and/or Azure, including RAG-based or agentic patterns. Exposure to model or LLM evaluation, telemetry, or feedback-loop tooling in support of safe and reliable AI operations. Level 18: Advanced education such as a Master's degree in Artificial Intelligence, Data Science, Computer Science, Engineering, or a closely related field. Relevant industry certifications, such as Databricks Data Engineer, Databricks GenAI Engineer, Azure AI Engineer Associate, Azure Solutions Architect Expert, Azure DevOps Engineer Expert, Azure Security Engineer, cloud security (e.g., CCSP), and/or Agile certifications (e.g., DASM). Hands-on experience with enterprise ML/AI Databricks Platform & Azure AI Foundry, including model lifecycle management and Git-based development workflows. Demonstrated delivery of production GenAI solutions in Databrick