Duration

12-Month Contract (Potential Extension)

Work Model

Hybrid (Downtown Toronto - 3 days in office)

Engagement Model

Qualified 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

5+ years of experience in MLOps, Machine Learning Engineering, AI Platform Engineering, Cloud Engineering, or related technical disciplines

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

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.

📌 Senior AI Platform Engineer (MLOps) (Toronto)
🏢 Akkodis
📍 Toronto