AI/ML Trainer

2 days ago

Pune, India Futurism Technologies, INC. Full time

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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