AI / ML Trainer

Job Title : 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).