Professor, Part-Time (Machine Learning Operations - MLOps)

Seneca•Ontario, CA

13d•Hybrid

About The Position

We are proud to be building the equitable Seneca, and welcome applications from candidates who can contribute to the diversity of lived experiences, perspectives and approaches. Joining Seneca means being part of an inclusive community working hard to dismantle barriers to access, student success and employee potential. If you require accommodation during any part of the recruitment process, please contact [email protected]. Do you believe in the power of education to transform lives and communities? Are you passionate about shaping the next generation of career-ready and world-ready individuals? If so, Seneca invites you to join our dynamic faculty in redefining the boundaries of teaching and learning. Joining Seneca you will be a part of a team that is committed to providing an education experience that goes beyond textbooks and classrooms ensuring that our students are equipped with the skills and knowledge needed to thrive in today's dynamic labour markets. As a professor at Seneca, you'll have the opportunity to be at the forefront of experiential learning, working closely with industry partners to provide students with real-world insights and opportunities, while also demonstrating teaching excellence, academic leadership, and expertise in curriculum development. Whether it's through entrepreneurship initiatives, applied research projects, or co-op placements, you'll play a pivotal role in shaping the future of our students. So, if you're ready to embrace flexibility, innovation, and the limitless potential of education, we invite you to join us on this journey. Together, let's redefine the future of education and empower the next generation of leaders, thinkers, and change makers. Here is What’s on Your Horizon Faculty/School: School of Software Design & Data Science Program/ Courses: MAI201 Master of Artificial Intelligence Design and Development (MAI) For Machine Learning Operations Courses Campus: Newnham We welcome applications for part-time teaching opportunities. Course assignments are based on scheduling and instructional needs. Contract faculty typically teach up to 6 hours per week. While some courses may be offered online or in a hybrid format, availability for on-campus teaching is often preferred. To help us align your application with the right opportunity, please include your availability when applying. Responsible for providing academic leadership in the classroom and ensuring an effective and compelling learning environment for students. You will…

Requirements

Ph.D. in Computer Science, Data Science, Artificial Intelligence/Machine Learning, or a closely related field.
Demonstrated expertise through research contributions or publications in areas such as ML deployment, model lifecycle management, or related domains.
Prior experience teaching graduate-level courses or delivering technical workshops in advanced topics.
Experience in the subjects taught is a requirement.
Demonstrated expertise through research contributions or publications in areas such as Machine Learning deployment, model lifecycle management, or related domains.
Prior experience teaching graduate-level courses or delivering technical workshops in advanced topics.
Hands-on experience deploying ML models in production environments.
Worked on projects involving scalability, reliability, and automation of ML workflows.
Collaborated with cross-functional teams (Data Engineering, DevOps)
Excellent communication and interpersonal skills.
Demonstrated exceptional presentation, communication, organizational, and interpersonal skills.
Commitment to a team approach for problem-solving and conflict resolution.
Commitment to equity in education.

Nice To Haves

Strong relationships with industry contacts and network connections are an asset.

Responsibilities

Ensure student awareness of course objectives, approach and evaluation techniques.
Carry out regularly scheduled instruction, which may include tutoring and academic counselling of students.
Provide a learning environment that makes effective use of available resources, work experience and field trips.
Evaluate student progress/achievement and assuming responsibility for the overall assessment of the student's work within assigned courses.

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