Manulife

Director,MLOps

$113–113k Toronto, Ontario, Canada FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Director candidates.

The Brief

“Director, MLOps at Manulife. Skills: MLOps, AI Engineering, solution architecture, cloud platforms, GenAI, agentic systems. architect and operationalize solutions that enable scalable, reusable, and well governed AI deployments. set the vision for MLOps in Canada”

What You'll Achieve.

scalable, reusable, and well governed AI deployments; measurable, sustainable business impact; AI initiatives translate into measurable, sustainable business impact

Industry & Context.

Problems you'll solve

architect and operationalize solutions; Design solutions; problem-solving

What They're Looking For.

Must Have

8+ years of experience in Data Science, Machine Learning Engineering, MLOps, or AI Engineering, Proven track record in designing and implementing scalable ML pipelines, real time/near real time scoring systems and AI platforms in enterprise environments, Expertise in model development validation, deployment, GenAI workflows, agent orchestration, and monitoring using modern cloud (Azure preferred) and MLOps frameworks, validation, and deployment using modern cloud technologies and MLOps frameworks, understanding of data assets, platforms, and advanced analytics concepts, Ability to lead cross-functional initiatives and collaborate with IT, InfoSec, data governance and global AI teams, Excellent communication skills, Master’s degree or equivalent experience in Computer Science, Data Science, Engineering, or related quantitative fields, Experience with agentic AI platforms, orchestration frameworks (e. g. , LangChain style, Azure Agent Services), Adaptive ML, Akka and safe integration into enterprise systems

Nice to Have

Familiarity Expertise with cloud AI platforms and enterprise architecture a nd modern LLMOps frameworks, Experience in financial services or regulated industries, Knowledge of ethical AI frameworks, model governance, security patterns for GenAI, and regulatory compliance (e. g. , model risk policies)

What You'll Do.

architect and operationalize solutions that enable scalable

and well governed AI deployments

set the vision for MLOps in Canada

drive alignment with our global AI organization and partnering technology teams

ensure consistency in patterns

and delivery practices

leading change management—helping teams adopt new processes

modernize ways of working

and increase trust in AI powered solutions

enabling fast learning cycles that accelerate innovation while maintaining high standards of security

influence enterprise architecture and ensure that AI initiatives translate into measurable

sustainable business impact

Define and architect MLOps capabilities and AI tooling

Hands-on implementation of MLOps solutions

integration with new platforms

enabling the team of data scientists with infrastructure

Design solutions that enable model development

CI/CD automation and monitoring

Implement and establish best practices for model validation

performance monitoring

and lifecycle management

Co-design solutions with global AIOps/MLOps teams

Drive R&D for advanced AI capabilities

ensuring integration with modern cloud-based platforms

agentic systems and next generation LLMOps tooling

Act as the subject matter expert for MLOps

guiding teams on automation

operational excellence

and model runtime optimization and operationalization of AI models

Ensure reliable environments (dev/test/prod/sandbox) are available

and ready for development and productionized AI workloads

Partners with product owners

business stakeholders and data scientists define clear pathways from experimentation to production

How You'll Work.

Team & Collaboration

partnering technology teams; Working across business, engineering, and data science teams; Co-design solutions with global AIOps/MLOps teams, IT, and data teams; Ability to lead cross-functional initiatives and collaborate with IT, InfoSec, data governance and global AI teams; Partners with product owners, business stakeholders and data scientists

Communication Scope

Excellent communication skills influence technical and business stakeholders

Process & Methodology

lead cross-functional initiatives

Full Job Description

This is a pivotal opportunity to shape Canada’s AI transformation through modern, enterprise grade MLOps capabilities. The Director will architect and operationalize solutions that enable scalable, reusable, and well governed AI deployments across segments. You will set the vision for MLOps in Canada, drive alignment with our global AI organization and partnering technology teams, and ensure consistency in patterns, tooling, and delivery practices. A critical part of the role includes leading change management—helping teams adopt new processes, modernize ways of working, and increase trust in AI powered solutions. The role also requires strong skills in experimentation and rapid prototyping, enabling fast learning cycles that accelerate innovation while maintaining high standards of security, compliance, and reliability. Working across business, engineering, and data science teams, you will influence enterprise architecture and ensure that AI initiatives translate into measurable, sustainable business impact. **Position Responsibilities:** * Define and architect MLOps capabilities and AI tooling to support Canada’s cross-segment initiatives and ensure scalability, governance and reuse. * Hands-on implementation of MLOps solutions, integration with new platforms and enabling the team of data scientists with infrastructure * Design solutions that enable model development, deployment, CI/CD automation and monitoring across multiple use cases. * Implement and establish best practices for model validation, risk assessment, performance monitoring, and lifecycle management. * Co-design solutions with global AIOps/MLOps teams, IT, and data teams to align with enterprise standards and leverage shared platforms. * Drive R&D for advanced AI capabilities, ensuring integration with modern cloud-based platforms, agentic systems and next generation LLMOps tooling. * Act as the subject matter expert for MLOps, guiding teams on automation, CI/CD for ML, observability, operational ex

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