MLOps Engineer

The opportunity

Manulife is seeking a motivated and dedicated Machine Learning Engineer with a focus on MLOps architecture, tooling, and standards development. In this key role, you will be responsible for maintaining and enhancing Python libraries, lightweight applications, and CI/CD pipelines that enable our AI/ML developers to efficiently track experiments, conduct testing, streamline deployment, and monitor model health. By using AI to more efficiently deliver AI, you will collaborate with AI/ML project teams to facilitate the adoption of MLOps tools and standards, incorporating feedback to continuously refine our tooling features. You will also contribute to the development of tools and standards to operationalize LLM and Generative AI use-cases, ensuring seamless integration with existing MLOps practices and infrastructure.

Responsibilities

• CI/CD Pipeline Management: Assist in implementing and managing CI/CD pipelines to automate testing and deployment processes, ensuring efficient and reliable operations.

• Collaboration and Support: Work with AI/ML project teams across segments to help them adopt MLOps tools and standards, providing guidance and support as needed.

• Stakeholder Engagement: Capture feedback from team members to improve and enhance MLOps tools and features, ensuring they meet the evolving needs of our teams.

• Standards Development: Contribute to defining and implementing effective practices and standards for MLOps processes to ensure efficient and streamlined operations.

• Industry Insights: Stay updated with industry advancements in LLM and Generative AI models, enhancing MLOps capabilities to support LLM Ops needs.

• Cross-Functional Collaboration: Work multi-functionally with data scientists, software engineers, architects, and operations teams to ensure flawless integration and operation of MLOps and LLM Ops solutions.

What we are looking for

• Educational Background: Bachelor's, Master's, or equivalent experience in Computer Science, Data Science, Statistics, or a related field.

• MLOps Experience: 3-4 years of experience in building and maintaining MLOps infrastructure and tooling.

• Software Development: Proficiency in Python and experience with developing libraries and applications.

• CI/CD Tools: Experience with CI/CD tools and pipelines, preferably Jenkins.

• Cloud and Containerization: Familiarity with cloud platforms (preferably Azure), ML development environments (Databricks), and containerization technologies (Docker, Kubernetes).

• Experiment Tracking and Monitoring: Exposure to experiment tracking tools (e.g., MLFlow) and monitoring solutions.

• Strong communication skills, able to explain complex concepts to various collaborators.

• Understanding of ML Lifecycle: Basic understanding of the machine learning lifecycle and standard methodologies in MLOps.

• LLM/GenAI Models: Basic understanding of LLM/GenAI models and their operational requirements.

Bonus Points:

• Databricks Ecosystem Expertise: Hands-on expertise in the Databricks ecosystem, particularly model management using Unity Catalog.

• Full Stack Engineering Exposure: Ability to build proof of concepts/demos including both front-end and back-end development.

• Adaptability: Demonstrated proficiency in quickly picking up new frameworks and libraries.

What can we offer you?

• A competitive salary and benefits packages.

• A growth trajectory that extends upward and outward, encouraging you to follow your passions and learn new skills.

• A focus on growing your career path with us.

• Flexible work policies and strong work-life balance.

• Professional development and leadership opportunities.

About Manulife and John Hancock

Manulife Financial Corporation is a leading international financial services provider, helping people make their decisions easier and lives better. To learn more about us, visit our story.

Manulife is an Equal Opportunity Employer

At Manulife/John Hancock, we embrace our diversity. We strive to attract, develop and retain a workforce that is as diverse as the customers we serve and to foster an inclusive work environment that embraces the strength of cultures and individuals.

Primary Location: Toronto, Ontario

Working Arrangement: Hybrid

Salary range is expected to be between: $75,880.00 CAD - $140,920.00 CAD