Head of Machine Learning Engineering Operations

MDA Edge; Edmonton; On-site; CAD 150,000 - 180,000; Full time

Job summary

A leading technology firm in Canada is seeking a Head of Machine Learning Engineering Operations to lead the MLOps team and oversee the development of AI products. Ideal candidates have over 10 years of experience, strong communication skills, and proficiency with various tools such as Github and MLflow. The role offers a competitive salary and full-time employment in the IT services industry.

Qualifications

• 10+ years of relevant experience.
• Experience in digital engineering and establishing engineering functions.
• Strong understanding of AI concepts and hands-on deployment experience.
• Excellent communication skills in English.

Responsibilities

• Work closely with managers to deliver AI products.
• Hire and build a high-performing MLOps team.
• Collaborate with engineering and product leadership on roadmap.
• Create processes enabling team members to excel.

Skills

MLOps

Github

GitAction

Splunk

cloudwatch

Argo

Spark

MLflow

Tools

Github

GitAction

Splunk

cloudwatch

Argo

Spark

MLflow

Job description

Head of Machine Learning Engineering Operations

4 weeks ago | Be among the first 25 applicants

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This range is provided by MDA Edge. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range: CA$150,000.00 / yr - CA$180,000.00 / yr

Key Responsibilities :

1. Work closely with managers across the data organization to provide resources and skills for AI product delivery.
2. Hire and build a high-performing MLOps team.
3. Lead client development standards and best practices.
4. Collaborate with engineering and product leadership to create and own the long-term roadmap and deliverables.
5. Communicate the roadmap and strategy effectively within the team and organization.
6. Create and manage processes enabling team members to excel.
7. Develop assets, accelerators, and thought leadership for your practice.
8. Design and build cloud-hosted client products with automated pipelines for model management.
9. Oversee AI/client app design and automated model/pipeline adaptation.
10. Work closely with solution architects, data scientists, and data engineers for validation.
11. Apply cloud architecture and DevOps expertise for operational AI solutions.
12. Lead end-to-end development of Ops pipelines based on cloud platforms and AI lifecycle understanding.
13. Research emerging tools and technologies; prototype and demonstrate solutions.
14. Craft solutions to complex problems using your judgment.
15. Support lifecycle management of deployed applications.
16. Develop and maintain MLOps standards, guides, and processes.
17. Guide stakeholders through solutions and product changes.
18. Maintain relationships with stakeholders for education and communication.
19. Lead teams to deliver results with sustainable practices.
20. Co-own project planning and releases.
21. Participate in architecture, design, code reviews, and hands-on development.
22. Lead and mentor a team of engineers and tech leads, fostering growth and excellence.
23. Participate in the engineering community and advise colleagues and stakeholders.

Key Requirements :

1. 10+ years of relevant experience.
2. Experience in digital engineering and establishing engineering functions.
3. Proficiency with tools like Github, GitAction, Splunk, cloudwatch, Argo, Spark, MLflow.
4. Experience establishing MLOps practices in complex environments.
5. Leadership in agile teams and building an agile culture.
6. Experience in building scalable AI products and setting product vision.
7. Ability to influence cross-functional stakeholders on impactful projects.
8. Leadership and coaching skills for team development.
9. Knowledge in data science, statistics, software engineering, and design thinking.
10. Experience with CI/CD pipelines, deployment, and lifecycle management in regulated environments.
11. Experience with data science applications at scale.
12. Strong understanding of AI concepts and hands-on deployment experience.
13. Ability to evaluate new technologies and document architecture decisions.
14. Excellent communication skills in English.

Required Skills and Certifications :

• MLOps
• Github
• GitAction
• Splunk
• cloudwatch
• Argo
• Spark
• MLflow

Seniority level: Mid-Senior level

Employment type: Full-time

Industry: IT Services and IT Consulting

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