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Key Responsibilities:
● Teach Applied AI/ML: Design and deliver practical, project-based courses in AI/ML
(Python for ML, Statistics, ML Algorithms, Deep Learning, NLP, CV, ML Ops, GenAI).
● Develop Industry-Relevant Curriculum: Help design and update the AI/ML curriculum
to reflect current industry tools, techniques, and best practices, incorporating your
professional experience and case studies.
● Mentor Student Projects: Guide students through hands-on AI/ML projects, providing
technical direction, code reviews, and feedback based on industry standards.
● Guide & Mentor Students: Advise students on developing practical skills,
understanding career paths in AI/ML, and preparing for internships and job placements.
● Stay Current: Bring the latest AI/ML research, tools, and industry trends into the
classroom.
● Collaborate: Work closely with other expert faculty and staff to create a unified and
effective learning experience.
● Assess Practical Skills: Design and evaluate assignments, projects, and assessments
focused on real-world applications.
Qualifications and Requirements:
● Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science,
AI/ML, or related field. (PhD is valued but not mandatory.)
● 5+ years of direct, hands-on professional experience in the tech industry as an
AI/ML Engineer, Data Scientist, Research Scientist, or similar role involving AI/ML
development and deployment.
● Proven Industry Track Record: Demonstrated experience in building, training, and
deploying machine learning models (including deep learning) for real-world problems.
● Deep AI/ML Understanding: Strong grasp of core ML algorithms (classical & deep
learning – CNNs, RNNs, Transformers), model evaluation, statistics, and awareness
of current research/industry trends.
● Passion for Teaching/Mentoring: Ability to explain complex concepts clearly and
guide others. Prior mentoring, corporate training, technical workshops, or project
supervision experience is highly relevant. (Formal academic teaching experience is
not mandatory.)
Required Skills:
● Expert-level Python programming.
● Proficiency with data science libraries (Pandas, NumPy, Scikit-learn).
● Hands-on experience with ML/DL frameworks (TensorFlow, PyTorch).
● Strong SQL and data handling skills.
● Understanding of ML Ops practices and tools (Git, Docker, AWS/GCP/Azure).
● Knowledge of key AI areas (NLP, Computer Vision, Generative AI/LLMs).
● Soft Skills: Strong communication, mentoring ability, collaboration, and a genuine
passion for education.
Good-to-Have:
● Prior teaching experience at the undergraduate or graduate level.
● Familiarity with modern teaching methodologies and academic tools.
