AI/ML Trainer

AI/ML Trainer

Location: Pune

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, or related field
• 3+ years of hands-on AI/ML experience (model development, deployment, or applied research).
• 2+ years of experience delivering technical training (classroom or online).
• Strong proficiency in Python, ML/DL libraries (TensorFlow, PyTorch, Scikit-learn), and data tools (Pandas, NumPy, SQL).
• Familiarity with Generative AI, LLMs, and prompt engineering is highly desirable.
• Excellent presentation, communication, and facilitation skills.

Preferred Skills

• Experience delivering corporate training programs or academic workshops.
• Knowledge of cloud ML platforms (AWS Sagemaker, Azure ML, GCP Vertex AI).
• Ability to simplify complex technical concepts into easy-to-understand lessons.
• Strong problem-solving mindset and learner-first approach.

Key Competencies

• Engagement-focused: Skilled in keeping learners actively involved.
• Clarity of instruction: Can explain complex AI/ML topics with real-world analogies.
• Adaptability: Flexible teaching style suited to different learner groups.
• Mentorship: Strong inclination to guide, mentor, and motivate learners.
• Continuous learner: Keeps up with evolving AI/ML trends and tools.

Key Responsibilities

Training Delivery

• Conduct live training sessions (in-person, virtual, or hybrid) for diverse groups including students, working professionals, and corporate teams.
• Teach core AI/ML topics such as supervised/unsupervised learning, deep learning, NLP, computer vision, generative AI, reinforcement learning, and MLOps.
• Guide learners through hands-on coding exercises, model building, and deployment practices.
• Facilitate Q&A, discussions, and problem-solving during training to ensure strong concept clarity.
• Demonstrate the use of industry-standard tools and frameworks such as Python, TensorFlow, PyTorch, Scikit-learn, Hugging Face, and cloud ML platforms.

Learner Engagement & Mentorship

• Provide individualized support to learners during training, helping them troubleshoot errors and understand best practices.
• Foster an interactive, motivating, and inclusive learning environment.
• Mentor learners on mini-projects and capstone assignments to ensure practical application of skills.
• Encourage collaboration through group exercises, coding challenges, and hackathon-style workshops.

Assessment & Feedback

• Evaluate learner performance through quizzes, assignments, and project reviews.
• Offer timely, constructive feedback to learners to accelerate their growth.
• Collect learner feedback after each session and adjust teaching methods to enhance learning outcomes.

Continuous Learning & Adaptation

• Stay up-to-date with latest advancements in AI/ML (Generative AI, LLMs, MLOps practices, etc.).
• Incorporate trending topics and practical industry use cases into session delivery.
• Adapt training content delivery to suit varying learner backgrounds (technical vs. non-technical).