Senior Instructor

Job Description: Senior Pharma AI/ML TrainerSenior Pharma AI/ML Trainer – Pharmacy & Life SciencesExperience Required: Minimum 8–10 years of relevant experience ONLY Employment Type: Full-Time / Part-Time / Visiting Faculty / Industry RELEVANT Expert ONLY Location: Pune / Hybrid / Pan-India Target Audience: B.Pharm, M.Pharm, Pharm.D and other Pharmacy & Life Sciences studentsAbout AI Cure AcademyKey Responsibilities1. Pharmaceutical AI/ML TrainingDesign and deliver advanced, practical training programs for:1.      B.Pharmacy students2.      M.Pharmacy students3.      Pharmacy faculty members4.      Life-science and biotechnology students5.      Working pharmaceutical professionals6.      Training should demonstrate how AI/ML can be practically applied to pharmaceutical research and development.2. AI in Drug DiscoveryThe trainer should be capable of teaching and demonstrating applications such as:1.      AI/ML-based drug discovery2.      Target identification and validation3.      Virtual screening4.      Molecular property prediction5.      QSAR and predictive modelling6.      Structure–activity relationship analysis7.      Drug–target interaction prediction8.      Molecular docking and computational drug discovery9.      Drug repurposing10.  ADMET prediction11.  Toxicity prediction12.  Lead identification and optimization13.  Generative AI for molecule generation14.  Deep learning applications in drug discovery3. AI in Clinical Research & Clinical TrialsThe trainer should have practical understanding of AI applications in:1.      Clinical trial design2.      Patient recruitment and matching3.      Patient stratification4.      Clinical data analysis5.      Predictive analytics6.      Clinical trial outcome prediction7.      Real-world evidence8.      Pharmacovigilance9.      Adverse-event prediction and analysis10.  Clinical data management11.  Medical and pharmaceutical data analytics4. AI & Machine LearningThe candidate should be able to teach concepts from beginner to advanced level, including:1.      Python for pharmaceutical applications2.      Machine Learning fundamentals3.      Supervised and unsupervised learning4.      Regression and classification5.      Clustering6.      Feature engineering7.      Model evaluation and validation8.      Deep Learning9.      Neural networks10.  Natural Language Processing11.  Generative AI12.  Large Language Models and their applications in Life Sciences13.  Data visualization14.  Statistical analysis15.  Pharmaceutical datasets and case studies5. Biotechnology & Computational BiologyKnowledge of AI applications in biotechnology and computational biology will be highly desirable, including:1.      Bioinformatics2.      Genomics3.      Proteomics4.      Computational biology5.      Protein structure prediction6.      Protein–ligand interaction analysis7.      Antibody discovery8.      Antibody engineering9.      Biomarker discovery10.  Omics data analysis11.  Biological sequence analysis7.      Practical & Project-Based Learning12.  The trainer will be responsible for developing hands-on projects, case studies and industry-oriented assignments.13.  Students should be able to complete projects such as:14.  AI-based drug discovery projects15.  Drug repurposing projects16.  QSAR modelling17.  ADMET prediction18.  Clinical trial analytics19.  Pharmacovigilance analytics20.  Molecular property prediction21.  Antibody/biologics analytics22.  Bioinformatics projects23.  Pharmaceutical data-science projectsThe trainer should focus on learning by doing, rather than only classroom-based theoretical instruction.Mandatory Eligibility CriteriaM.Pharm / Pharm.D / Ph.D in Pharmacy or Pharmaceutical Sciences; ORM.Tech / M.Sc / Ph.D in Biotechnology, Bioinformatics, Computational Biology, Data Science, Artificial Intelligence, Machine Learning or a closely related discipline,Candidates with only general AI/ML industry experience but without substantial experience in Pharmacy education/training will not be preferred.Mandatory Domain KnowledgeThe candidate should have demonstrated knowledge and/or practical experience in multiple areas of:1.      Pharmacy2.      Pharmaceutical sciences3.      Artificial Intelligence4.      Machine Learning5.      Data Science6.      Drug Discovery7.      Clinical Research / Clinical Trials8.      Biotechnology9.      Bioinformatics10.  Computational BiologyPreferred Candidate ProfileWe are particularly interested in candidates who have worked across both Pharmacy and AI/ML and can explain complex technology in a manner that is understandable and relevant to pharmacy students.The ideal candidate should be able to answer:·      "How can a B.Pharm or M.Pharm student use AI and Machine Learning to solve a real pharmaceutical problem?"·      The candidate should be capable of converting pharmaceutical concepts into practical AI/ML applications and guiding students from fundamentals → practical training → projects → industry applications.Teaching & Training ExpectationsThe trainer will be expected to:1.      Develop structured curriculum and course content2.      Prepare practical laboratory exercises3.      Create pharmaceutical AI/ML case studies4.      Conduct live demonstrations5.      Mentor student projects6.      Conduct assessments and evaluations7.      Guide students in research projects8.      Conduct workshops and masterclasses9.      Train faculty members where required10.  Keep curriculum updated with emerging AI technologies11.  Connect classroom learning with pharmaceutical industry requirements12.  Guide students toward internships, research and industry opportunitiesRequired Technical SkillsThe candidate should have working knowledge of relevant tools and technologies, depending on specialization:1.      Programming & Data Science2.      Python3.      SQL4.      Pandas5.      NumPy6.      Scikit-learn7.      Jupyter8.      AI/ML9.      Machine Learning10.  Deep Learning11.  NLP12.  Generative AI13.  LLM applications14.  Predictive analytics15.  Pharmaceutical / Bioinformatics16.  RDKit17.  Molecular modelling tools18.  Bioinformatics platforms19.  Drug databases20.  Molecular docking platforms21.  ADMET prediction tools22.  Protein/antibody analysis tools23.  Knowledge of cloud platforms and modern AI development environments will be an advantage