Senior AI / Ml Trainer

Key Responsibilities
Conduct engaging in-person and virtual training sessions across a range of learner profiles
Deliver workshops and bootcamps on AI/ML, Deep Learning, Generative AI, RAG pipelines, and Agentic AI systems and LLMOps.
Design and develop training materials, including slide decks, hands-on labs, demo notebooks, video walkthroughs, and assessments aligned towards current industry trends and customer needs.
Build modular, reusable learning content covering GenAI application development, RAG architectures, LLM fine-tuning, prompt engineering, and multi-agent orchestration, LLMOps.
Integrate real-world use cases and project-based learning into every module, ensuring practical applicability.
Work closely with internal and external Subject Matter Experts (SMEs) to draft training proposals and curate content aligned with enterprise skilling goals.
Train and mentor internal trainers and partner faculty to scale delivery across regions and formats.
Requirements (Must- have):
ML and Deep learning frameworks: Scikit Learn, TensorFlow, PyTorch, Keras
Neural network architectures: CNNs, RNNs, LSTMs, Transformers
ML workflows: data preprocessing, model training, evaluation, and optimization
Working knowledge of Large Language Models (LLMs): GPT, Llama, Mistral, Gemini, Claude, etc.
Prompt Engineering: zero-shot, few-shot, chain-of-thought, and advanced prompting patterns
RAG (Retrieval-Augmented Generation), Vector databases (Pinecone, FAISS, Chroma, Weaviate), embedding models, chunking strategies
LLM Fine-tuning: LoRA, QLoRA, PEFT, RLHF
Agentic AI & Frameworks:
Building and teaching AI Agents — tool use, memory, planning, and reasoning loops
Hands-on familiarity with at least two or more Agentic AI frameworks: LangChain/LangGraph, LlamaIndex, CrewAI, AutoGen (Microsoft), OpenAI Agents SDK or Anthropic Claude SDK
Hands-on knowledge of MCP (Model Context Protocol), tool calling, and function calling patterns
Hands-on knowledge of Multi-Agent system design and its nuances and LLMOps
Qualifications
Bachelor's degree in computer science, Engineering, Mathematics, or a related field or equivalent practical experience
Demonstrated hands-on experience in AI/ML model development, GenAI application building, or data science workflows
Proven track record of delivering technical training, workshops, or bootcamps especially in AI/ML or GenAI domains
Experience with cloud-based AI/ML platforms (AWS Sagemaker, Azure AI Foundry, GCP Vertex AI, etc.) is highly recommended.
Having any of professional cloud certifications is a plus
Prior experience developing content for professional certifications or enterprise skilling programs is a plus
Excellent verbal and written communication skills, with the ability to explain complex technical concepts to diverse audiences
Strong facilitation skills - comfortable engaging beginners as well as seasoned engineers in the same session
Strong analytical and problem-solving skills with an eye for real-world applicability
Good to have:
Contributions to open-source AI/ML projects or public learning resources (blogs, notebooks, talks)
Experience with AI governance, responsible AI practices, or bias/safety considerations in LLMs
Active community presence (LinkedIn, Hugging Face, GitHub, etc.)