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Added 15 mins ago
TT
Digital Controls Technical Specialist
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- Full-time
…driving analytics for greater efficiency, your ideas will help engineer solutions for stronger communities and a sustainable…
Added 15 mins ago
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Software Engineer - Product Lifecycle Information Management
lululemon
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- Full-time
Added 46 mins ago
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electrical engineer
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- $80,000 to $135,000 CAD / year
- Full-time
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Electrical Engineer ? Government & Institutional Projects *Location:* Vancouver /BC Area preferred *Employment:* Full-Time, Permanent *Salary:* CAD $80,000?$135,000 per year, depending on…
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Job Details: Senior Infrastructure Engineer (Linux & Cloud Automation)
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Senior QA Automation – SDET
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- Full-time
CGI is seeking an experienced Senior QA Automation Engineer to join our growing Quality Engineering team. This is a hands-on role for a quality engineering professional with strong…
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Added 1 hour ago
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…(CROR) for Canada, General Code Operating Rules (GCOR) for U.S., Engineer, etc.) as per industry…
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Manager Operating Practices
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Electrical Engineer
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Controls Engineer
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- Full-time
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Added 2 hours ago
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QA Support Engineer
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Added 2 hours ago
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Added 2 hours ago
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Added 2 hours ago
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Research Engineer - Instrumentation
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Akkodis

Senior AI Platform Engineer (MLOps)

Toronto, ON, Canada

Contract

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Job details

Job type: Contract
Apply by: Nov 4, 2026
Experience: 5+ years of experience in MLOps, Machine Learning Engineering, AI Platform Engineering, Cloud Engineering, or related technical disciplines
Posting language: English
Posted: Oct 5, 2026

Original job description

Title: Senior AI Platform Engineer

Position Type: Contract

Duration: 12-Month Contract (Potential Extension)

Client Location: Toronto, Ontario

Work Model: Hybrid (Downtown Toronto - 3 days in office)

Engagement Model: Professional Services Engagement – B2B (Incorporated Consultants Preferred)

About the Engagement

Akkodis is partnering with a leading institutional investment and pension organization to engage an experienced Senior AI Platform Engineer (MLOps) to support the ongoing growth and operationalization of enterprise AI capabilities.

This is a highly technical, hands-on engineering role focused on building, deploying, governing, and operating machine learning and AI platforms at enterprise scale. The successful candidate will work closely with AI Governance, Cybersecurity, Enterprise Architecture, Platform Engineering, and AI Centre of Excellence teams to implement responsible AI controls, operationalize machine learning models, and establish scalable AI deployment standards.

While the role supports AI governance and responsible AI initiatives, the primary focus is technical implementation. The client is seeking a strong engineering professional with deep MLOps, machine learning operationalization, cloud engineering, and Python development experience who can translate governance requirements into automated controls, deployment processes, and platform capabilities.

This opportunity is ideal for someone who enjoys solving complex technical problems, building enterprise-grade AI capabilities, and working across infrastructure, machine learning, security, and platform engineering domains.

Services to be Provided:

Design, build, and maintain enterprise MLOps and LLMOps capabilities supporting AI and machine learning solutions
Develop and automate model deployment, monitoring, evaluation, governance, and lifecycle management processes
Build CI/CD pipelines and Infrastructure as Code solutions supporting AI and machine learning environments
Implement model governance, auditability, approval workflows, and operational controls within production AI platforms
Support implementation of responsible AI and AI governance requirements through technical controls and automation
Develop and maintain model tracking, lineage, experiment management, and reproducibility capabilities
Implement and enhance MLflow, Databricks, evaluation frameworks, and related platform services
Collaborate with Cybersecurity, Enterprise Architecture, and AI Governance teams to operationalize enterprise requirements
Build and support AI deployment patterns for machine learning, RAG, and generative AI solutions
Troubleshoot, debug, and resolve platform, deployment, performance, and operational issues
Create technical documentation, implementation standards, operational procedures, and support guides
Support platform reliability, observability, monitoring, incident management, and recovery processes
Implement cloud cost monitoring, optimization, and consumption management practices
Provide technical guidance and enablement to teams adopting enterprise AI capabilities
Communicate technical solutions and implementation outcomes to both technical and senior business stakeholders

Expertise Required

5+ years of experience in MLOps, Machine Learning Engineering, AI Platform Engineering, Cloud Engineering, or related technical disciplines
Strong hands-on Python development experience
Proven experience operationalizing machine learning models in enterprise production environments
Experience designing and supporting model lifecycle management, deployment, monitoring, and governance processes
Strong understanding of MLOps best practices, CI/CD, automation, testing, and software engineering principles
Experience with Databricks, MLflow, or comparable machine learning platform technologies
Experience working with cloud platforms such as Azure, AWS, or Google Cloud
Experience with Infrastructure as Code methodologies and tooling
Hands-on experience with Terraform and modern deployment automation practices
Experience with GitHub Actions, Jenkins, Azure DevOps, or similar CI/CD technologies
Experience troubleshooting complex distributed systems and production environments
Strong understanding of machine learning model operationalization, evaluation, and observability
Experience implementing secure deployment and access control patterns within cloud or AI environments
Excellent communication skills with the ability to engage both technical and non-technical stakeholders
Proven ability to operate independently and deliver within fast-paced and evolving environments

How to Apply

If you are interested in learning more, don't hesitate to apply today or check out Akkodis Canada website for more opportunities.

Important

This is a business-to-business engagement. Candidates must represent an incorporated entity, hold a valid business number, maintain appropriate insurance, and invoice for services rendered.

We thank all applicants for their interest in this opportunity. Only candidates meeting the above qualifications will be contacted for further discussions.

Accessibility:

At Akkodis, part of The Adecco Group, our purpose is simple: to make the future work for everyone. We live our values, Passion, Collaboration, Inclusion, Courage, and Customers at Heart, by fostering a workplace where diversity is celebrated and every voice matters. We encourage applications from individuals of all backgrounds and identities. Together, we’re making the future work for everyone.

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More about this job

Responsibilities

Design, build, and operate enterprise MLOps and LLMOps capabilities, including automated deployment, monitoring, evaluation, governance, and lifecycle management for machine learning and generative AI solutions. Implement secure, reliable, and cost-aware platform processes and controls, and collaborate with governance, cybersecurity, architecture, and engineering teams to establish standards and support adoption.

Requirements

Requires at least five years of relevant MLOps, machine learning engineering, AI platform, or cloud engineering experience, strong Python skills, and experience operationalizing models in enterprise production environments. Candidates should have hands-on experience with platforms such as Databricks or MLflow, cloud services, Terraform and Infrastructure as Code, CI/CD, model lifecycle processes, and production troubleshooting.

Skills

Python
MLOps
LLMOps
Machine Learning
Databricks
MLflow
Cloud Engineering
Terraform
Infrastructure As Code
CI/CD
Model Deployment
Model Monitoring
Model Governance
Generative AI
Retrieval-Augmented Generation
Observability

Visa sponsorship

Not detected in the job text

Keywords

MLOps
LLMOps
Python
Machine Learning
AI Platform Engineering
Databricks
MLflow
Azure
AWS
Google Cloud
Terraform
Infrastructure As Code
CI/CD
GitHub Actions
Jenkins
Azure DevOps
Model Deployment
Model Lifecycle Management
Model Monitoring
Model Evaluation
Model Governance
Experiment Management
Model Lineage
Responsible AI
Generative AI
Retrieval-Augmented Generation
Cloud Engineering
Observability
Access Control
Cost Optimization

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