MLOps Engineer building production ML infrastructure, pipelines, and monitoring for exacare ai’s post - acute care AI platform. Scaling reliable systems that help healthcare teams make safer placement decisions.
EC
AI and Machine Learning Engineer
Posted last month
About the role
- Ingénieur IA chez EDC, société d’État canadienne facilitant l’expansion internationale des entreprises. Déploiement et gouvernance de systèmes d’IA et d’apprentissage machine en production.
Responsibilities
- Design and maintain reusable machine learning assets, including feature pipelines, shared components, deployment templates, and evaluation frameworks
- Collaborate with data scientists, architects, and platform and security teams to transform research models into reliable, scalable production services
- Design and deploy production-ready machine learning and generative AI solutions
- Manage or support the full lifecycle of AI systems, including versioning, monitoring, retraining, scaling, rollback, and decommissioning
- Implement generative AI applications based on retrieval-augmented generation (RAG) and agentic models
- Integrate security, governance, responsible AI, auditability, and risk management controls
- Automate deployments using CI/CD practices, infrastructure as code, and standardized environments
- Monitor, diagnose, and resolve performance issues, data and model drift, anomalies, costs, and production incidents
- Design and validate predictive, descriptive, and behavioral models related to operational performance indicators
- Design supervised and unsupervised learning algorithms for structured data
- Prepare data in collaboration with stakeholders and internal clients
- Develop concepts and prototypes for new AI products and services
- Balance performance, scalability, cost, security, and risk management in enterprise AI systems
Requirements
- University degree in computer science, engineering, mathematics, data science, or a related technical discipline
- Level 17: At least 5 years of experience in AI or machine learning engineering involving production systems
- Level 17: At least 5 years of experience designing large-scale data platforms, primarily Databricks and Azure
- Level 17: At least 3 years of hands-on experience with production machine learning or AI systems
- Level 17: At least 3 years of experience with Azure cloud platforms, deployment, automation, networking, and security, with a focus on Databricks
- Level 17: At least 3 years of experience with continuous integration, continuous deployment, and infrastructure as code for AI
- Level 17: At least 3 years of experience developing production-ready code using Python and data-focused languages, including SQL, Java, or Scala
- Level 17: At least 3 years of experience with formal IT service management and Agile implementation
- Level 17: At least 1 year of hands-on experience with generative AI or machine learning operations in production
- Level 18: At least 7 years of experience in AI or machine learning engineering involving production systems
- Level 18: At least 7 years of experience designing large-scale data platforms, primarily Databricks and Azure
- Level 18: At least 7 years of hands-on experience with production machine learning or AI systems
- Level 18: At least 7 years of experience with Azure cloud platforms and Databricks data operations
- Level 18: At least 7 years of experience with continuous integration, continuous deployment, and infrastructure as code for AI
- Level 18: At least 5 years of experience developing production-ready code using Python and SQL, Java, or Scala
- Level 18: At least 7 years of experience with IT service management and Agile implementation
- Level 18: At least 3 years of hands-on experience with generative AI or machine learning operations in production
- Fluency in both of Canada’s official languages: English and French
- Candidates must meet government security requirements
- Experience deploying containerized AI workloads and scalable cloud infrastructure
- Knowledge of AI security, governance, and risk management frameworks
- Knowledge of or experience with MITRE ATLAS, MITRE ATT&CK, OWASP LLM Top 10, OWASP ML Top 10, ISO/IEC 42001, ISO/IEC 27001, NIST 800-53, HITRUST, ENISA, and the EU AI Act
- Level 18: Hands-on experience with Databricks and Azure AI Foundry, model lifecycle management, and Git workflows
- Level 18: Experience delivering production-ready generative AI solutions, including RAG and agentic models
- Level 18: Experience with model and large language model evaluation, telemetry, and feedback loops
- Level 18: Experience collaborating with external vendors on managed services or professional services projects
- Canadian citizenship or permanent residency is preferred to work legally in Canada at the time of application
Benefits
- Performance-based compensation
- Competitive compensation and benefits package
- Various hybrid work options
- Three to four weeks of vacation
- Company-wide shutdown period
- Shorter summer Fridays
- No-meeting Fridays
- Training programs and workshops
- Language training
- Diverse and inclusive workplace
- Wellness initiatives
- Mental health support
- Fitness programs
- Volunteer activities and social responsibility programs
- Relocation assistance for eligible candidates
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