AI/ML Trainer

19 hours ago

Pune, Maharashtra, India Futurism Technologies, INC. Full time ₹ 12,00,000 - ₹ 24,00,000 per year

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).

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