About the Role :

We are looking for a passionate Senior Technical Trainer with good hands-on to join our expert team. Candidate will drive knowledge adoption across customers, partners, and internal teams delivering high-impact training in AI/ML, Deep Learning, and the rapidly evolving Generative AI and Agentic AI landscape.

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 and 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.)