Assistant Professor - Centre of Artificial Intelligence

Assistant Professor – Centre of Artificial Intelligence

Role Profile

Position Details

Position: Assistant Professor

Department: Centre of Artificial Intelligence

Reports To: Dean/Director/Head, Centre of Artificial Intelligence

Role Type: Teaching, Research, Training, Innovation, and University-wide AI Integration

Job Purpose

The Assistant Professor will contribute to teaching, research, innovation, capacity building, and AI adoption across the university. The role involves delivering AI-related courses, conducting interdisciplinary research, supporting faculty and students, developing AI literacy initiatives, and enabling responsible AI integration in academic and administrative functions.

Educational Qualifications

Essential

• Qualifications as per applicable UGC/AICTE/University norms.
• B.E./B.Tech. and M.E./M.Tech. in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Information Technology, Electronics, Robotics, Cybersecurity, or allied disciplines; OR
• Master's Degree in AI, Computer Science, Data Science, Statistics, Mathematics, Computational Sciences, Information Technology, Computer Applications, or related fields.

Desirable

• Ph.D. in Artificial Intelligence, Machine Learning, Data Science, Computer Science, Engineering, or interdisciplinary AI domains.
• UGC-NET/SET/SLET qualification where applicable.
• Publications in reputed indexed journals and conferences.
• Experience in funded projects, consultancy, patents, technology development, or AI innovation.
• Professional certifications and hands-on expertise in AI tools, Python, machine learning frameworks, cloud platforms, and Generative AI.

Required Competencies

Technical Skills

• Strong knowledge of AI, Machine Learning, Data Science, Deep Learning, NLP, Computer Vision, and Generative AI.
• Proficiency in Python and AI/ML libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, OpenCV, and Hugging Face.
• Experience with data analytics, model development, cloud AI platforms, and prompt engineering.
• Understanding of AI ethics, privacy, cybersecurity, transparency, and responsible AI practices.

Academic & Training Skills

• Ability to teach undergraduate, postgraduate, diploma, certificate, and executive programs.
• Curriculum development, instructional design, assessment creation, and project-based learning.
• Capability to conduct workshops, FDPs, AI literacy programs, and hands-on training sessions.
• Student mentoring for projects, internships, hackathons, research, patents, and entrepreneurship.

Research & Innovation Skills

• Research publication and proposal-writing capabilities.
• Ability to develop interdisciplinary AI projects and industry collaborations.
• Experience supporting innovation, patents, consultancy, and technology development.

Institutional AI Integration

• Ability to identify and implement AI applications across academic and administrative departments.
• Development of AI-enabled teaching, research, productivity, and automation solutions.
• Capacity to train faculty, staff, and students in practical AI adoption.

Experience

Essential

• Teaching, research, or industry experience in AI, ML, Data Science, Computer Science, IT, or allied areas.
• Ability to teach theory and laboratory courses in AI-related domains.

Desirable

• 1–5 years of relevant experience.
• Experience in curriculum design, AI projects, training programs, industry collaboration, research, patents, or AI laboratory development.

Key Responsibilities

Teaching & Academic Activities

• Deliver courses in AI, Machine Learning, Data Science, Python, Analytics, Generative AI, and emerging technologies.
• Develop course materials, laboratory manuals, assignments, and assessments.
• Conduct practical sessions and promote experiential learning.
• Mentor students for projects, internships, competitions, and research activities.
• Support development of new AI-focused academic programs and interdisciplinary offerings.

Research & Publications

• Conduct high-quality research and publish in reputed journals and conferences.
• Develop interdisciplinary research initiatives.
• Prepare research proposals for external funding.
• Support patents, innovation, consultancy, and industry-sponsored projects.

University-Wide AI Integration

• Facilitate AI adoption across academic schools and administrative units.
• Assist faculty in integrating AI tools into teaching, research, content development, and analytics.
• Conduct AI literacy and awareness programs for stakeholders.
• Support development of AI policies, implementation plans, and governance frameworks.
• Promote ethical, responsible, and transparent AI use.

Laboratory & Infrastructure Development

• Support establishment and operation of AI laboratories, software platforms, and computing infrastructure.
• Maintain technical resources, datasets, documentation, and learning environments.
• Coordinate with industry partners for laboratory enhancement and collaborative initiatives.

Training & Capacity Building

• Conduct faculty development programs on AI, Generative AI, data analytics, and educational technologies.
• Deliver student training programs in AI, ML, Python, and emerging technologies.
• Train non-teaching and administrative staff on AI-enabled productivity and automation tools.
• Develop structured learning resources and training materials.

Industry Collaboration & Outreach

• Build partnerships with industry, start-ups, research institutions, and government organizations.
• Facilitate internships, certifications, live projects, consultancy assignments, and collaborative programs.
• Organize conferences, workshops, hackathons, expert talks, and innovation events.
• Represent the Centre in academic and industry forums.

Institutional Responsibilities

• Assist in planning, documentation, reporting, and implementation of AI initiatives.
• Participate in committees, curriculum development, examinations, admissions, and accreditation-related activities.
• Support documentation for NAAC, NBA, NIRF, rankings, and quality assurance processes.

Expected Outcomes

• Effective delivery of AI-related academic programs.
• Enhanced AI literacy and adoption across the university.
• Increased research publications, funded projects, patents, and innovations.
• Strong industry-academia collaborations.
• Development of the Centre of Artificial Intelligence as a leading hub for teaching, research, training, and innovation.
• Improved student employability, entrepreneurship, and future-ready skills.

Key Performance Indicators (KPIs)

• Teaching effectiveness and student outcomes.
• Research publications, patents, grants, and consultancy.
• AI integration initiatives across departments.
• Training programs conducted and stakeholders trained.
• Laboratory utilization and project support.
• Industry partnerships, internships, and collaborative activities.
• Contributions to institutional development and accreditation.