Data Science Trainer - AI/ML

AI/ML Mentor:

Experience: 4–5+ years | Role: AI/ML Mentor | Work Mode: Full-time

Role Overview:

We are looking for an AI/ML Mentor with strong hands-on expertise across Machine Learning, Deep Learning, Generative AI, and AI application development. The role combines learner mentoring with technical academic responsibilities, including session delivery, query handling, doubt sessions, project creation and evaluation, and assessment development.

Key Resonsibilities:

• Deliver AI/ML sessions with strong conceptual clarity, hands-on coding, and real-world industry use cases.
• Conduct doubt-clearing and mentoring sessions and provide timely technical guidance.
• Handle and resolve learner technical queries through defined support channels.
• Create and maintain AI/ML projects, use cases, datasets, and project documentation aligned with industry requirements.
• Evaluate learner projects, code, implementations, presentations, and technical understanding using defined rubrics.
• Create and maintain assessments, coding exercises, assignments, and scenario-based technical questions.
• Guide learners across the complete journey: Learning → Practice → Assessment → Project → Evaluation.
• Support learners in implementing AI/ML solutions from problem understanding to deployment.
• Identify recurring learner challenges and contribute to improvements in curriculum, projects, assessments, and learning content.
• Ensure consistency in technical accuracy, delivery quality, evaluation standards, and learner experience.

Tools and Technology Stack:

Core:

Python | SQL | Pandas | NumPy | Matplotlib/Seaborn | Scikit-learn | Statistics | Machine Learning | Feature Engineering | Model Evaluation

Deep Learning & AI:

PyTorch / TensorFlow | Neural Networks | CNN | Computer Vision | NLP | Transformers

Generative AI:

LLMs | Prompt Engineering | RAG | Vector Databases | LangChain / LlamaIndex | AI Agents | Multimodal AI

Development & Deployment:

Flask / FastAPI | REST APIs | Git/GitHub | Docker | MLOps | Model Deployment

Cloud – AWS:

AWS AI/ML services | SageMaker | S3 | IAM | Cloud-based Model Deployment | Basic AWS Architecture

Key Requirements:

• Strong hands-on expertise in AI/ML and Deep Learning.
• Practical experience with NLP, Transformers, LLMs, RAG and/or AI Agents.
• Ability to build practical AI/ML projects and assessments.
• Strong experience in query handling, project evaluation, doubt sessions, and learner mentoring.
• Excellent technical communication with the ability to simplify complex concepts.
• Ability to connect technical concepts with real-world AI applications and production workflows.
• Exposure to AWS and MLOps/deployment is preferred.
• Role Success Criteria

AI/ML Expertise | Clear Delivery | Query Resolution | Project & Assessment Quality | Learner Mentoring | Industry Relevance