Ingénieur(e) En Gestion De Données ML

Ericsson•Montreal, QC

11h•Onsite

About The Position

This role involves piloting the design, integration, and launch of a key SaaS/PaaS offering for data management and MLOps. This includes a data lakehouse, ETL/ELT pipelines, a data catalog, operational observability capabilities, and ML training and inference environments. The position requires close collaboration with system architects and infrastructure teams to define the technical vision and roadmap for the platform. A scalable and secure multi-tenant SaaS infrastructure must be designed, ensuring data isolation, role-based access control (RBAC), and rigorous data governance.

Requirements

Engineering degree or Master's (Bac +5) in Computer Science, Software Engineering, or equivalent field.
5 to 8 years of progressive experience for a senior profile (and 10 years or more for an expert level) in data engineering, software engineering, platform development, or MLOps, with proven experience in designing and operating large-scale distributed systems.
Advanced proficiency in Python, Go, and SQL.
Solid hands-on experience with Docker, Kubernetes, Helm/Flux, and IaC and GitOps tools (ideally Flux / ArgoCD).
Concrete experience in data management, deployment automation, and operational execution.
Preferred knowledge of equivalent infrastructure services from major cloud providers (AWS, Azure, GCP) and prior experience with hybrid cloud SaaS environments.
Proficiency in data orchestration tools (Apache Airflow, Dagster) and data warehouse/lakehouse architectures.
Familiarity with data lakehouse architectures (Apache Iceberg / Delta Lake), Apache Spark, Kafka, and open table formats.
Experience with ML platforms such as JupyterLab, SageMaker, Vertex AI, or equivalent ML training/inference environments.
Proven ability to design and deploy robust, scalable, and secure data lake and data warehouse architectures.
Deep knowledge of data governance principles, cataloging, traceability (lineage), personal data (PII) masking, and regulatory compliance.
Expertise in deploying MLOps best practices (model lifecycle management, deployment, monitoring, and retraining).
Mastery of IT security best practices, including Zero Trust architecture, Identity and Access Management (IAM), and protection of data in transit and at rest.
Ability to lead technical discussions, make architectural decisions, mentor junior profiles, and unite stakeholders.
Excellent writing and interpersonal skills, service/product-oriented approach, and a desire to provide a smooth experience for development teams.
Ability to work in a team while also working independently, methodical management of priorities, and natural curiosity for emerging technologies and complex challenges.
Bilingual French and English is required.

Nice To Haves

Master's degree is a strong asset.

Responsibilities

Pilot the design, integration, and launch of a key SaaS/PaaS offering for data management and MLOps, including a data lakehouse, ETL/ELT pipelines, data catalog, operational observability capabilities, and ML training and inference environments.
Collaborate closely with system architects and infrastructure teams to define the technical vision and roadmap for the platform.
Design a scalable and secure multi-tenant SaaS infrastructure, ensuring data isolation, role-based access control (RBAC), and rigorous data governance.
Develop and maintain automated feature stores with strict versioning and compliance metadata.
Design advanced CI/CD workflows with automated evaluation milestones to detect model drift, test for bias, and validate data quality.
Design and develop an intuitive self-service onboarding, observability, and troubleshooting ecosystem for MLOps and data management services.
Create and standardize reusable data pipeline templates, ETL/ELT components, project structures, and custom CLI tools to accelerate deployment and adoption by teams.
Manage bidirectional, secure, and auditable data flows between internal and third-party services, implementing governance and traceability features (personal/PII data masking, tokenization, malware detection, provenance tracking).
Evolve with ease in multi-technology and multi-platform IT environments.
Act as a technical reference and mentor for junior engineers, promoting best practices in engineering, design, and operations.
Continuously drive and optimize the developer onboarding journey on the platform, write clear and up-to-date technical documentation, and actively engage with the user community to gather feedback.
Define and track developer productivity metrics (e.g., build times, PR validation times, DORA/SPACE metrics) to proactively identify and resolve friction points.
Actively contribute to the creation of accelerators and reusable model pipelines, ensuring regulatory compliance and model ethics from the design phase (ethics by design).
Apply and evolve SRE/DevOps/SecOps practices (CI/CD pipelines, continuous monitoring with feedback loops, global observability, and GitOps-based Infrastructure-as-Code ecosystems) to ensure services are maintained in the target state.
Qualify and resolve internal tool and platform malfunctions, providing responsive support and clear communication during critical incidents.

Benefits

Choice of 3 medical and dental plan options.
Core level coverage paid for fully by Ericsson.
Automatic 2% company contribution to the Pension Plan.
50% match of employee's contribution into the Registered Retirement Savings Plan, up to 8% of the employee’s contribution (maximum of 4% match).
Basic life insurance and basic accidental death and dismemberment coverage at two-times annual base pay at no cost.
Short-term disability coverage.
Option to participate in Ericsson’s Stock Purchase Plan.
Minimum of 18 days of accrued vacation.
At least 3 personal days.
Minimum 10 holidays.
1 volunteer day.
Sick days.
Up to 10 weeks of paid maternity leave.
6 weeks of parental or adoption leave at 100% of pay.
Financial wellness programs.
Educational assistance.
Matching gifts.
Wellness account.
Recognition programs.