: Senior Pharma AI/ML Trainer

Pharmacy, Pharmaceutical Sciences & Life Sciences

Organization: AiCure Academy

Experience: Minimum 8–10 years of relevant experience

Employment Type: Full-Time / Part-Time / Visiting Faculty / Industry Expert

Location: Pune / Hybrid / Pan-India

Target Audience: B.Pharm, M.Pharm, Pharm.D, Pharmacy Faculty, and Life Sciences Students

About AiCure AcademyAiCure Academy is dedicated to bridging the gap between pharmaceutical sciences and emerging Artificial Intelligence technologies. Our mission is to equip pharmacy and life sciences students with practical, industry-relevant AI/ML skills that can be applied across drug discovery, clinical research, pharmaceutical data analytics, biotechnology, and computational biology.

We are looking for experienced pharmaceutical and AI/ML professionals who can translate complex scientific and technological concepts into practical, hands-on learning experiences.

Key Responsibilities1. Pharmaceutical AI/ML Training

- Design and deliver structured, practical AI/ML training programs for B.Pharm, M.Pharm, Pharm.D, and other life sciences students.
- Train pharmacy faculty members and working pharmaceutical professionals.
- Demonstrate real-world applications of AI/ML in pharmaceutical research, development, and industry workflows.
- Develop beginner-to-advanced learning pathways tailored to pharmacy and life sciences backgrounds.

2. AI in Drug Discovery and DevelopmentDeliver practical training in areas including:
- AI/ML-based drug discovery and target identification and validation.
- Virtual screening and computational drug discovery.
- Molecular property prediction and quantitative structure–activity relationship (QSAR) modelling.
- Structure–activity relationship (SAR) analysis.
- Drug–target interaction prediction.
- Molecular docking and protein–ligand interaction analysis.
- Drug repurposing and lead identification and optimization.
- ADMET and toxicity prediction.
- Generative AI and deep learning for molecule generation and drug development.

3. AI in Clinical Research and Clinical Trials
- Apply AI/ML to clinical trial design and optimization.
- Demonstrate AI-driven patient recruitment, matching, and stratification.
- Develop predictive models for clinical trial outcomes.
- Teach clinical data analysis, clinical data management, and pharmaceutical analytics.
- Introduce real-world evidence (RWE) and predictive analytics.
- Apply AI to pharmacovigilance, adverse-event detection, prediction, and analysis.
- Demonstrate data-driven approaches to improving clinical research workflows.

4. Artificial Intelligence, Machine Learning, and Data Science




- Teach Python programming for pharmaceutical and life sciences applications.
- Cover machine learning fundamentals, including supervised and unsupervised learning.
- Explain regression, classification, clustering, and feature engineering.
- Teach statistical analysis, data visualization, model evaluation, and validation.
- Introduce deep learning, neural networks, and natural language processing (NLP).
- Demonstrate Generative AI, Large Language Models (LLMs), and their applications in life sciences.
- Develop practical exercises using pharmaceutical datasets and industry-oriented case studies.

5. Biotechnology and Computational BiologyKnowledge of the following areas will be highly desirable:
- Bioinformatics and computational biology.
- Genomics, proteomics, and omics data analysis.
- Biological sequence analysis and biomarker discovery.
- Protein structure prediction and protein–ligand interaction analysis.
- Antibody discovery and antibody engineering.
- AI applications in biotechnology, biologics, and molecular research.

6. Practical Training, Projects, and Mentorship
- Develop hands-on laboratory exercises, industry-oriented assignments, and real-world case studies.
- Guide students through end-to-end projects, from problem identification to model development and evaluation.
- Mentor students undertaking research projects, capstones, and pharmaceutical data science assignments.
- Conduct live demonstrations, workshops, and advanced masterclasses.
- Assess student progress through practical evaluations and project reviews.

Expected student project areas include:

- AI-based drug discovery and drug repurposing.
- QSAR modelling and molecular property prediction.
- ADMET and toxicity prediction.
- Clinical trial analytics and predictive modelling.
- Pharmacovigilance and adverse-event analytics.
- Antibody and biologics analytics.
- Bioinformatics and omics data analysis.
- Pharmaceutical data science and AI-driven research.

The emphasis should be on learning by doing, enabling students to apply their knowledge to real pharmaceutical and life sciences problems rather than relying solely on theoretical instruction.



Mandatory Eligibility CriteriaCandidates must hold a relevant postgraduate or doctoral qualification in one or more of the following disciplines:

- M.Pharm, Pharm.D, or Ph.D. in Pharmacy or Pharmaceutical Sciences.
- M.Tech, M.Sc., or Ph.D. in Biotechnology, Bioinformatics, Computational Biology, Data Science, Artificial Intelligence, Machine Learning, or a closely related discipline.

Candidates must also demonstrate substantial relevant skilled, research, teaching, or training experience, with a minimum of 8–10 years of relevant experience. Important: Candidates with general AI/ML industry experience alone, without substantial exposure to pharmaceutical sciences, pharmacy education, pharmaceutical research, or life sciences applications, will not be preferred.

Mandatory Domain KnowledgeCandidates should demonstrate practical knowledge and/or professional experience across multiple areas, including:

- Pharmacy and Pharmaceutical Sciences.
- Artificial Intelligence and Machine Learning.
- Data Science and Predictive Analytics.
- Drug Discovery and Development.
- Clinical Research and Clinical Trials.
- Biotechnology and Bioinformatics.
- Computational Biology.
- Pharmaceutical Data Analytics.

Required Technical SkillsCandidates should have working knowledge of relevant tools and technologies aligned with their area of specialization.

Programming and Data Science

- Python and SQL.
- Pandas and NumPy.
- Scikit-learn.
- Jupyter Notebook and related development environments.
- Statistical analysis, data visualization, and data preprocessing.

Artificial Intelligence and Machine Learning

- Machine Learning and Deep Learning.
- Natural Language Processing (NLP).
- Generative AI and Large Language Models (LLMs).
- Predictive analytics and model evaluation.
- Practical AI application development.

Pharmaceutical AI and Computational Research

- RDKit and cheminformatics tools.
- Molecular modelling and molecular docking platforms.
- Pharmaceutical and molecular databases.
- ADMET prediction and toxicity assessment tools.
- Bioinformatics platforms and biological data analysis tools.
- Protein structure, protein–ligand interaction, and antibody analysis tools.

Knowledge of cloud platforms, modern AI development environments, and research-oriented computational tools will be an added advantage. Teaching and Training ExpectationsThe selected trainer will be expected to:
1. Develop structured curricula, course modules, and training materials.
2. Design practical laboratory exercises and pharmaceutical AI/ML case studies.
3. Conduct live demonstrations, workshops, and masterclasses.
4. Mentor students through projects, capstones,

📌 AI in Pharmaceutical Sciences – Senior Trainer / Subject Matter Expert (Pune)
🏢 AI Cure Academy
📍 Pune