Lead ML Engineer - MLOps​/LLMOps

As a Lead ML Engineer focusing on MLOps and LLMOps at Manulife, you will be at the heart of a data-driven culture! You will serve as the product owner for Manulife's MLOps framework, standards, and guidelines, maintaining the central Git Hub MLOps repository, and fostering a community around it.

You’ll spearhead the global MLOps rollout in Azure platform by collaborating with AI/ML teams and partners across the organization. You’ll foster the global adoption of MLOps practices by working closely with Lead Data Scientists, Lead ML Engineers, and Architects.

You’ll also own the global rollout of Continuous Monitoring capabilities, working closely with cloud and 3rd party vendors ensuring reliability and scalability, and maintaining comprehensive documentation of MLOps processes and procedures!

You will streamline the AI ML model deployment and monitoring efficiency by developing MLOps strategies, best practices, and standards. You’ll be responsible for the design, deployment, and management of scalable and reliable infrastructure for model training and deployment.

Responsibilities

Serve as the product owner for Manulife's MLOps framework, standards, and guidelines, which includes maintaining the central Git Hub MLOps repository and fostering a community around it.

Lead the global MLOps rollout in the Azure platform by collaborating with AI/ML teams and partners across the organization.

Foster the global adoption of MLOps practices by working closely with Lead Data Scientists, Lead ML Engineers, and Architects.

Oversee the global rollout of Continuous Monitoring capabilities, collaborating closely with cloud and 3rd party vendors to ensure reliability and scalability.

Maintain comprehensive documentation of MLOps processes and procedures to ensure transparency and efficiency.

Develop MLOps strategies, best practices, and standards to streamline the AI ML model deployment and monitoring efficiency.

Oversee the design, deployment, and management of scalable and reliable infrastructure for model training and deployment.

Build and lead a strong MLOps team, nurturing a culture of excellence, and encouraging professional growth and development across the organization.

Stay updated with higher-level trends in LLMs and open‑source platforms to incorporate the latest technology and tools into the framework.

Build and maintain a centralized code base or framework used by multiple teams.

Work with infrastructure as code like Terraform to manage and provision the technology stack for an application through code, improving overall efficiency and reducing human error.

What motivates you?

You obsess about customers, listen, engage and act for their benefit.

You think big, with curiosity to discover ways to use your agile approach and enable business outcomes.

You thrive in teams and enjoy getting things done together.

You take ownership and build solutions, focusing on what matters.

You do what is right, work with integrity and speak up.

You share your humanity, helping us build a diverse and inclusive work environment for everyone.

What we are looking for

Minimum Qualifications:

7+ years of combined experience as an ML engineer, Dev Ops engineer, system architect or lead data scientist in Big Data ecosystems or similar distributed or public cloud platforms.

2-3 years of hands‑on experience with MLOps, particularly Databricks and MLFlow in Azure stack, Jenkins, Kubernetes, Docker.

Experience in deploying ML models either via native infrastructure (such as Azure Dev Ops etc.) or 3rd party MLOps platforms.

Proficiency with SQL, Python, PySpark for model development and ML Ops.

Proven leadership and coaching skills to develop individuals and teams.

Experience in data science, statistics, software engineering, modular design, and design thinking.

Preferred Qualifications:

Experience with LLMs (extractive and generative), fine‑tuning, and operationalizing LLM pipelines.

Familiarity with higher‑level trends in LLMs and open‑source platforms.

Experience in building and maintaining a centralized code base or framework used by multiple teams will be a plus.

Experience with infrastructure as code like Terraform.

Excellent communication and…