Senior AI Platform Engineer (MLOps)

Job summary

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.

Job details

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.

What you’ll do

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.

Listed skills

• CI/CD · Preferred
• Machine learning · Preferred
• Terraform · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• 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

Job areas

• Technology
• Software
• Engineering
• Data & Analytics
• Consulting

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