Machine learning ops engineer Jobs

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- Full-time
- Azure
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Product Manager - AI Platform
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- Full-time
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- Machine learning
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Product Manager - AI Platform
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- Full-time
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- Full-time
- SQL
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Element Fleet Management —Toronto, ON
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Ericsson —Montréal, QC
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Senior Consultant, ML/AI Engineer, Data & AI
KPMG —Toronto, ON
- $73,000–$102,000 a year
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- Microsoft Excel
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Senior Character Technical Artist - EA SPORTS Technology
Electronic Arts —Vancouver, BC
- $122,300–$170,700 a year
- Full-time
- Bachelor's
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- Scripting
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Technical Artist - EA SPORTS Technology
Electronic Arts —Vancouver, BC
- $104,500–$142,800 a year
- Full-time
- Computer science
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Senior DevOps Engineer, for AI-based Systems
Oncoustics —Toronto, ON
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Global Solutions Manager - Physical AI
Vention —Montréal, QC
- Full-time
- Hybrid work
- Travel
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- Restaurant experience
- Work from home
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Senior Technical Artist (Rigging) - EA SPORTS Technology
Electronic Arts —Vancouver, BC
- $141,400–$204,400 a year
- Full-time
- Scripting
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- Disability insurance
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14d
Senior Manager, Data Security Automations & Engineering (Global Security)
Royal Bank of Canada —Halifax, NS
- Full-time
- Computer Science
- Master's degree
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13d
Senior Manager, AWS Cloud Architecture, Global Deloitte Technology
Deloitte —Toronto, ON
- $104,000–$215,000 a year
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Machine Learning Ops Engineer
Stratum AI —Canada
- Full-time
- Remote
- Azure
- Computer Science
- Engineering
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Data Engineer
Lime —Canada
- $121,000–$147,000 a year
- Full-time
- Remote
- Computer Science
- Engineering
- Master's degree
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7d
Principal Incident Response Engineer 1
Sophos —Canada
- $161,000–$268,000 a year
- Weekends as needed
- Azure
- AWS
- Analysis skills

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AI Engineer

Chubb -
Toronto, ON

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Job details

Full-time

Qualifications

Azure
Oracle
Law
Financial auditing
React
Kubernetes
PyTorch
Full-stack development
SAP
DevOps
Data structures
ERP systems
System architecture
Google Cloud Platform
Enterprise architecture
Application development
AWS
Docker
C
Machine learning
Natural language processing
GitHub
AI
Jenkins
GitLab
Communication skills
Python

Full job description

KEY OBJECTIVES:

We are seeking a highly skilled AI Engineer to lead the design, solutioning, and development of scalable, reusable AI-driven core capabilities for our global Finance Transformation program. This role sits at the intersection of finance, technology, and governance, enabling the development of intelligent, compliant-by-design solutions that support key Finance functions at a global level.

You will partner closely with cross-functional teams including Finance, IT, Global Analytics, Legal, Compliance, and Risk to deliver secure, scalable, and regulatory-aligned AI solutions across the enterprise.

MAJOR RESPONSIBILITIES:

Lead the architecture, design, and development of AI/ML solutions at a global level to support finance and controllership processes (e.g., close, reconciliation, reporting, anomaly detection etc).
Build scalable, reusable core AI capabilities that can be leveraged across multiple finance use cases and geographies.
Translate business requirements into robust technical solutions, ensuring alignment with enterprise architecture and data strategy.
Collaborate with Finance, IT, Data & Analytics, Legal, Compliance, Security, Enterprise Architecture and Risk teams to design and implement compliant-by-design AI solutions.
Ensure adherence to regulatory requirements, data privacy laws, and internal governance frameworks.
Develop and deploy models using modern AI/ML frameworks; ensure model performance, monitoring, and lifecycle management.
Identify opportunities to automate and optimize finance processes using AI (e.g., intelligent automation, NLP, predictive analytics).
Provide technical leadership and mentorship to junior engineers and cross-functional teams.
Ensure high quality code that meets business objectives, quality standards and development guidelines.
Building reusable pipelines, processes, and tools to streamline LLM and generative AI workflows while driving adoption of MLOps best practices, including CI/CD pipelines, versioning, testing, and model governance.
Manage project stakeholder expectations and issue communications on progress.
React to shifting priorities without compromising deadlines and momentum.
Stay current with emerging AI technologies and assess their applicability within finance and risk-controlled environments.

QUALIFICATIONS:

Must have:
- 2 - 5 years’ experience in AI Engineering and/or Machine Learning (ML) with a focus on LLMs, with deep expertise in writing, and reviewing production code in Python
- Understanding the development lifecycle for LLMs— developing data sets for pre-training, instruction tuning, and preference alignment alongside the modelling techniques for each stage and LLM deployment is as MAJOR plus
- Strong knowledge of LLM frameworks and libraries (such as transformers, trl, deepspeed, PyTorch), and exposure to various ML techniques and their practical implementation in production at large scale
- Experience building and deploying solutions on cloud platforms (AWS, Azure, or GCP)
- Experience on distributed, high throughput and low latency architectures
- Strong fundamentals in NLP techniques for text representation, semantic extraction techniques, data structures and modeling
- Experience building software on top of major container technology (Kubernetes, Docker etc.)
- Knowledge of version control using Jenkins, GitHub Actions, GitLab CI, Jenkins, or Azure DevOps
- Solid understanding of data engineering concepts and working with large-scale datasets
- Experience implementing ML Ops practices and production-grade AI systems

Nice to have:
- Experience and Knowledge of Finance Domain: Understanding of finance concepts, workflows, or platforms is a strong asset for this role along with Knowledge of financial processes such as close, consolidation, reporting, and audit
- Exposure to regulatory and compliance frameworks (e.g., SOX, GDPR, model risk management)
- Experience with ERP systems (e.g., SAP, Oracle) and finance data ecosystems
- Experience defining system architecture and exploring technical feasibility tradeoffs is a plus
- Strong understanding of AI risk, explainability, and auditability
- Familiarity with end-to-end application development using full stack is a plus

Key Competencies:
- Strong problem-solving and analytical thinking
- Ability to work across cross-functional and global teams
- Excellent communication and stakeholder management skills
- High attention to governance, risk, and compliance considerations

What Success Looks Like:
- Delivery of scalable AI capabilities embedded within finance processes
- Measurable improvements in key KPIS - efficiency, accuracy, and compliance
- Strong adoption of AI solutions across finance teams globally

Chubb Canada does not use artificial intelligence (AI) tools to assess, screen, or select applicants.

At Chubb we are committed to providing equal employment opportunities to all employees and applicants. It is our policy to provide equal employment opportunities to employees and applicants based on job-related qualifications and ability to perform a job. If you require accommodation during the hiring process or upon hire, please inform Human Resources. If a selected applicant requests accommodation during the recruitment process, Chubb will consult with the applicant in order to provide suitable accommodation that takes into account the applicant’s accessibility needs.

Apply Now

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