Title: Machine Learning Engineer - Senior -Hybrid

Title: Machine Learning Engineer - Senior

Duration

6 Months

Location

Toronto, ON, Canada

Onsite Flexibility

50% Remote

Introduction

The role will design and evolve enterprise data architecture and data models to enable scalable analytics, AI, and data products. The position will help establish the future-state data architecture, identify critical data assets, and define standards and integration patterns that enable data to be governed, reusable, accessible, and AI-ready.

Required Skills & Qualifications

• Degree in Computer Science, Information Systems, Data Management, Engineering, or a related discipline, or equivalent experience.
• Significant experience in enterprise data architecture, data modelling, data engineering, or related disciplines.
• Demonstrated experience developing conceptual, logical, and physical data models and enterprise data architectures.
• Experience designing modern data architectures supporting cloud, analytics, AI/ML, and enterprise integration.
• Strong knowledge of data modelling, data integration, APIs, data platforms, metadata, master/reference data, and data quality concepts.
• Prior work experience at client or in client's Industry.

Preferred Skills & Qualifications

• Understanding of data governance, data ownership, lineage, security, privacy, and information lifecycle management.
• Knowledge of modern architecture approaches such as data lakehouse, data products, domain-oriented architecture, and cloud-based data platforms.
• Ability to translate complex business and technical requirements into practical architecture standards, models, and patterns.

Day-to-Day Responsibilities

• Design and maintain enterprise data architecture and conceptual, logical, and physical data models supporting AI, analytics, and operational use cases.
• Define scalable architecture patterns for the integration, storage, transformation, sharing, and consumption of enterprise data.
• Design reusable data products and common data models that can support multiple business and AI use cases.
• Contribute to the organization's future-state data architecture, including transition from existing platforms and architectures.
• Identify and define critical data assets and domains required to support enterprise priorities.
• Establish and promote enterprise data architecture, modelling, interoperability, and integration standards.
• Partner with Data Governance teams to embed data ownership, quality, metadata, lineage, classification, and lifecycle requirements into architecture and data products.
• Assess data requirements and architecture implications for AI, Generative AI, machine learning, advanced analytics, and reporting initiatives.
• Work with business, Data & AI, architecture, engineering, and technology teams to translate business requirements into scalable data solutions.
• Identify architecture gaps, duplication, data silos, and opportunities to improve data reuse and interoperability across the enterprise.

Company Benefits & Culture

• Competitive salary and benefits package.
• Opportunities for professional development and career growth.
• Inclusive and collaborative work environment.

For immediate consideration please click APPLY to begin the screening process with Alex.