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Visiting Faculty​/Instructor – Computer Vision

Job in Bengaluru, 560001, Bangalore, Karnataka, India
Listing for: AlgoTutor
Part Time position
Listed on 2026-07-01
Job specializations:
  • Education / Teaching
    IT/Computer Science Instructor, University Professor, Computer Science, Online Teaching
Job Description & How to Apply Below
Position: Visiting Faculty / Instructor – Computer Vision
Location: Bengaluru

Algo Tutor  is a leading EdTech company committed to making quality tech education affordable, practical, and accessible. We partner with colleges, universities, and institutions to deliver tailored on-campus and online upskilling programs that align with academic schedules and prepare students for the industry.

We are currently seeking  passionate industry professionals  who would like to contribute to student development as Visiting Faculty / Part-Time Instructors for our Dev Ops & Cloud Training Program conducted for students at our partner colleges.

Work Location & Duration:
College Campus, Electronic City, Bengaluru
2-3 Sessions per week
Program Duration: 6 Weeks
Session Duration: 2 Hours
Tentative Start Date : 1st Aug 2026

What You Will Do / Responsibilities
Teaching & Delivery
Deliver instructor-led sessions as part of the Computer Vision curriculum.
Teach core computer vision and deep learning concepts with a strong balance of theory, intuition, and hands-on implementation.
Conduct interactive lectures, including whiteboard explanations, coding walkthroughs, architectural breakdowns, and problem-solving discussions.
Guide students through practical implementations, labs, assignments, and computer vision-based case studies and projects.

Topics Covered
The course broadly covers foundational and intermediate concepts in Computer Vision and Deep Learning, including CNNs, transfer learning, embeddings, object detection and segmentation, generative models, and practical computer vision workflows using Python-based deep learning libraries. The curriculum combines theoretical understanding with hands-on implementation, projects, and real-world applications.

Student Engagement & Mentorship
Support students in developing strong conceptual clarity and practical problem-solving skills in computer vision and deep learning.
Provide guidance on assignments, labs, projects, and debugging approaches.
Address student queries and facilitate technical discussions to deepen learning.
Mentor students on experimentation, model evaluation, and best practices in computer vision workflows.

Course Delivery Excellence
Align with the existing curriculum structure, evaluation methods, and academic expectations.
Collaborate with the academic team to ensure smooth course execution.
Contribute feedback on curriculum, assignments, and assessment quality.
Help maintain a high bar for academic rigor and classroom engagement.

Required Qualifications
Education
Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, Computer Vision, or a related field.

Experience
2–6+ years of relevant experience in Computer Vision, Deep Learning, AI/ML, or related domains (industry or academia).
Prior teaching, training, mentoring, or technical content delivery experience is strongly preferred.
Hands-on experience building or deploying deep learning or computer vision systems in real-world scenarios.

Knowledge, Skills, and Abilities
Strong understanding of deep learning fundamentals and computer vision concepts.
Familiarity with CNNs, transfer learning, embeddings, object detection/segmentation, and generative models.
Proficiency in Python and familiarity with frameworks such as PyTorch, Tensor Flow, OpenCV, or similar libraries.
Ability to explain complex concepts in a clear, structured, and engaging manner.
Strong communication, classroom management, and presentation skills.
High ownership and reliability in a part-time teaching setup.

Application Process

Shortlisted candidates will undergo:
Technical / Knowledge Round  – Assessment of deep learning and computer vision fundamentals, architectural understanding, and applied problem solving
Teaching Round  – Demo lecture or topic delivery
Fitment Round  – Alignment with academic expectations and teaching philosophy

Join us in empowering the next generation of tech professionals!
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