: Senior Pharma AI/ML Trainer

Senior Pharma AI/ML Trainer – Pharmacy & Life Sciences

Experience 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 students

About AI Cure Academy Key Responsibilities

- Pharmaceutical AI/ML Training

Design and deliver advanced, practical training programs for:

- B.Pharmacy students
- M.Pharmacy students
- Pharmacy faculty members
- Life-science and biotechnology students
- Working pharmaceutical professionals
- Training should demonstrate how AI/ML can be practically applied to pharmaceutical research and development.
- AI in Drug Discovery The trainer should be capable of teaching and demonstrating applications such as:
- AI/ML-based drug discovery
- Target identification and validation
- Virtual screening
- Molecular property prediction
- QSAR and predictive modelling
- Structure–activity relationship analysis
- Drug–target interaction prediction
- Molecular docking and computational drug discovery
- Drug repurposing
- ADMET prediction
- Toxicity prediction
- Lead identification and optimization
- Generative AI for molecule generation
- Deep learning applications in drug discovery 3. AI in Clinical Research & Clinical Trials The trainer should have practical understanding of AI applications in:
- Clinical trial design
- Patient recruitment and matching
- Patient stratification
- Clinical data analysis
- Predictive analytics
- Clinical trial outcome prediction
- Real-world evidence
- Pharmacovigilance
- Adverse-event prediction and analysis
- Clinical data management
- Medical and pharmaceutical data analytics 4. AI & Machine Learning The candidate should be able to teach concepts from beginner to advanced level, including:
- Python for pharmaceutical applications

2.

Machine

Learning fundamentals

- Supervised and unsupervised learning
- Regression and classification
- Clustering
- Feature engineering
- Model evaluation and validation
- Deep Learning
- Neural networks

10.





Natural Language

Processing

- Generative AI

12.

Large Language

Models and their applications in Life Sciences

- Data visualization
- Statistical analysis
- Pharmaceutical datasets and case studies 5. Biotechnology & Computational Biology

Knowledge of AI applications in biotechnology and computational biology will be highly desirable, including:

- Bioinformatics
- Genomics
- Proteomics
- Computational biology
- Protein structure prediction
- Protein–ligand interaction analysis
- Antibody discovery
- Antibody engineering
- Biomarker discovery
- Omics data analysis
- Biological sequence analysis
- Practical & Project-Based Learning
- The trainer will be responsible for developing hands-on projects, case studies and industry-oriented assignments.
- Students should be able to complete projects such as:
- AI-based drug discovery projects
- Drug repurposing projects
- QSAR modelling
- ADMET prediction
- Clinical trial analytics
- Pharmacovigilance analytics
- Molecular property prediction
- Antibody/biologics analytics
- Bioinformatics projects
- Pharmaceutical data-science projects The trainer should focus on learning by doing, rather than only classroom-based theoretical instruction.

Mandatory Eligibility

Criteria

M.Pharm / Pharm.D / Ph.D in Pharmacy or Pharmaceutical Sciences; OR

M.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 Knowledge The candidate should have demonstrated knowledge and/or practical experience in multiple areas of:





- Pharmacy
- Pharmaceutical sciences

3.

Artificial

Intelligence

4.

Machine

Learning

- Data Science
- Drug Discovery

7.

Clinical

Research / Clinical Trials

- Biotechnology
- Bioinformatics

10.

Computational Biology Preferred Candidate

Profile

We 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 Expectations The trainer will be expected to:

- Develop structured curriculum and course content
- Prepare practical laboratory exercises
- Create pharmaceutical AI/ML case studies
- Conduct live demonstrations
- Mentor student projects
- Conduct assessments and evaluations
- Guide students in research projects
- Conduct workshops and masterclasses
- Train faculty members where required
- Keep curriculum updated with emerging AI technologies
- Connect classroom learning with pharmaceutical industry requirements
- Guide students toward internships, research and industry opportunities Required Technical Skills The candidate should have working knowledge of relevant tools and technologies, depending on specialization:
- Programming & Data Science
- Python
- SQL
- Pandas
- NumPy
- Scikit-learn
- Jupyter
- AI/ML

9.

Machine

Learning

- Deep Learning
- NLP
- Generative AI
- LLM applications
- Predictive analytics
- Pharmaceutical / Bioinformatics
- RDKit
- Molecular modelling tools
- Bioinformatics platforms
- Drug databases
- Molecular docking platforms
- ADMET prediction tools
- Protein/antibody analysis tools
- Knowledge of cloud platforms and modern AI development environments will be an advantage

📌 Senior Instructor (Delhi)
🏢 AI Cure Academy
📍 Delhi