Technical Trainer

AI/ML Trainer

Key Responsibilities

• Design, develop, and deliver comprehensive training programs in

  AI/ML and GenAI

  domains.
• Conduct hands-on workshops and sessions covering

  TensorFlow, PyTorch, Scikit-learn, Deep Learning, and MLOps best practices

  .
• Develop and deliver advanced sessions on

  Generative AI

  , including

  OpenAI API usage, LangChain integration, GPT-based application development, and prompt engineering techniques

  .
• Create high-quality learning materials: presentations, lab guides, coding exercises, and real-world project assignments.
• Mentor learners through coding challenges, model development, and deployment exercises.
• Stay updated with the latest research, frameworks, and tools in AI/ML and GenAI to continuously enhance training content.
• Collaborate with stakeholders to assess skill gaps and customize training modules for corporate and academic audiences.
• Provide guidance on

  MLOps workflows

  , covering CI/CD for ML models, model monitoring, and scalable deployment.
• Evaluate participant performance and provide constructive feedback.

Required Skills & Qualifications

• 4–12 years of experience

  in AI/ML engineering, research, or training.
• Proficiency in

  TensorFlow, PyTorch, Scikit-learn, and Deep Learning architectures (CNNs, RNNs, Transformers, GANs, etc.)

  .
• Strong understanding of

  MLOps pipelines, model deployment, monitoring, and scaling

  .
• Expertise in

  Generative AI tools and frameworks

  :
• OpenAI APIs (ChatGPT, GPT-4, Embeddings, Fine-tuning).
• LangChain for building AI-powered applications.
• Prompt engineering and optimization techniques.
• GPT integration into enterprise applications.
• Solid programming background in

  Python

  and experience with data preprocessing, model building, and deployment.
• Familiarity with

  cloud platforms

  (AWS, GCP, or Azure) for AI/ML deployment.
• Excellent presentation, communication, and mentoring skills.
• Prior experience in delivering

  technical training programs, workshops, or corporate upskilling

  initiatives.

Preferred Qualifications

• Certifications in

  TensorFlow, PyTorch, or cloud-based ML services

  .
• Hands-on experience with

  LLM fine-tuning and vector databases (e.g., Pinecone, FAISS, Weaviate)

  .
• Experience with

  Kubernetes, Docker, and MLflow

  for MLOps.
• Exposure to real-world AI/ML projects across domains such as NLP, Computer Vision, and Recommendation Systems.