Ingénieur(e) En Gestion De Données ML

Ericsson•Montreal, QC

2d•Onsite

About The Position

This role involves leading the design, integration, and launch of an end-to-end Data Management and MLOps SaaS/PaaS offering. The position requires architecting scalable and secure multi-tenant SaaS infrastructure, building automated feature stores, developing advanced CI/CD workflows, and creating an intuitive self-service ecosystem for data management and MLOps services. The role also includes acting as a technical leader and mentor, optimizing developer onboarding, and applying SRE/DevOps/SecOps practices to ensure service reliability. The position requires the ability to work with multiple technologies and IT platforms, manage bidirectional data flows, and ensure data governance and security.

Requirements

Bachelor's degree in Computer Science, Software Engineering, or a related field; a master's degree is a strong asset.
Minimum 5-8 years for senior, (and 10+ for expert level ) of progressive experience in data engineering, software engineering, platform development, or MLOps, with proven experience designing and operating large-scale systems.
Proficiency in Python, Go, and SQL.
Deep experience with Docker, Kubernetes, Helm/Flux, IaC and Gitops tools (preferably Flux / Argocd).
Hands-on experience with data mgmt., automation for deployment and operations
Preferably knowledge of similar stacks on hyperscale cloud provider services (AWS, Azure, GCP) and prior experience with hybrid cloud SAAS setups/ environments.
Experience with data orchestration tools (Apache Airflow, Dagster) and data warehouse/lakehouse platforms.
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 implement robust, scalable, and secure data lake and data warehouse architectures.
Deep knowledge of data governance concepts, cataloging, lineage, PII masking, and regulatory compliance.
Expertise in implementing MLOps best practices, including model lifecycle management, deployment, monitoring, and retraining.
Strong understanding of IT security best practices, including Zero Trust architecture, Identity and Access Management (IAM), and protection of data at rest and in transit.
Ability to lead technical discussions, make architectural decisions, mentor junior engineers, and influence stakeholders.
Excellent written and verbal communication skills, with a product- and service-oriented approach, capable of creating low-friction experiences for development teams.
Collaborative team player with the ability to work independently, manage priorities, and an insatiable curiosity for new technologies and complex challenges.
Bilingual French and English is required for this position.

Responsibilities

Lead the design, integration, and launch of an end-to-end Data Management and MLOps SaaS/PaaS offering, including a data lakehouse, ETL/ELT pipelines, data catalog, operational observability capabilities, and ML training/inference environments.
Collaborate closely with end-to-end architects and infrastructure teams to define the technical vision and roadmap for the platform.
Architect scalable and secure multi-tenant SaaS infrastructure, ensuring tenant data isolation, role-based access control (RBAC), and robust data governance.
Build and maintain automated feature stores with strict versioning and compliance metadata.
Develop advanced CI/CD workflows with automated gates for model drift, bias testing, and data quality validation.
Design and develop an intuitive service onboarding and observability/troubleshooting ecosystem for our Data Management and MLOps services, including self-service capabilities.
Build and standardize reusable data pipeline templates, ETL/ELT components, project scaffolding, and custom CLI tools to accelerate delivery and onboarding.
Manage secure, auditable, and bidirectional data flows across internal and third-party services, implementing data governance and lineage capabilities (PII masking, tokenization, malware detection, provenance tracking).
Capable to work on multi-technologies and IT platforms.
Act as a technical leader and mentor for junior engineers, fostering best practices in engineering, design, and operations.
Own and continuously improve the developer onboarding lifecycle on the platform, create clear and up-to-date technical documentation, and engage with the user community to gather feedback.
Establish and track developer productivity metrics (e.g., build times, PR cycle times, DORA/SPACE metrics) to proactively identify and address friction points.
Actively contribute to reusable model pipelines and accelerators that enforce regulatory compliance and model ethics by design.
Apply and evolve SRE/DevOps/SecOps practices, including CI/CD pipelines, feedback-loop-based monitoring, end-to-end observability, and GitOps-based Infrastructure-as-Code (IaC) ecosystems to maintain the desired state of services.
Triage and troubleshoot internal tooling and platform issues, providing attentive 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 into 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