Job Description: Experience Required: Minimum 4-5 years of professional experience in AI/ML, Data Science, or Applied Machine Learning. Position Summary:We are seeking a passionate and technically strong Senior Trainer - Artificial Intelligence & Machine Learning to deliver our advanced AI curriculum focused on LLMs, Retrieval-Augmented Generation (RAG), Agentic AI, and end-to-end deployment.
The ideal candidate will have a deep understanding of modern AI architectures and the ability to mentor learners in building autonomous, production-grade AI systems - integrating retrieval pipelines, intelligent agents, and deployment workflows across real-world scenarios.
Key Responsibilities- Deliver engaging, project-based sessions on advanced topics in AI, LLMs, and agentic AI development.
- Train and mentor learners on:
- Core AI/ML concepts: supervised & unsupervised learning, deep learning, and NLP.
- Large Language Models (LLMs): transformer architecture, fine-tuning, and prompt optimization
- Retrieval-Augmented Generation (RAG): vector databases, document retrieval, embeddings, and knowledge-grounded responses.
- Agentic AI Systems:
- Designing and orchestrating AI agents capable of autonomous decision-making
- Using LangGraph, CrewAI, or AutoGen for multi-agent frameworks
- Integrating external tools, APIs, and reasoning loops for dynamic task execution
- Understanding memory management, context persistence, and tool use in agent frameworks
- AI Deployment & MLOps:
- Building scalable APIs with FastAPI or Flask
- Model packaging and orchestration with Docker, Kubernetes, and CI/CD pipelines
- Model tracking, experimentation, and monitoring with MLflow, Weights & Biases, or Vertex AI Pipelines.
- Cloud AI Integration: deploying and managing systems on AWS (SageMaker), Azure ML, or GCP Vertex AI.
- Lead hands-on projects where learners build RAG-based chatbots, autonomous AI assistants, and deployed LLM applications.
- Collaborate on curriculum development to integrate cutting-edge AI research and tools into the training modules.
- Mentor learners through technical challenges, performance optimization, and model deployment.
- Keep up to date with LLM, agentic AI, and generative AI innovations to ensure curriculum relevance.
Required Skills & Qualifications- Experience: 4 to 5+ years in AI/ML engineering, Data Science, Applied NLP, or MLOps roles.
- Technical Expertise:
- Proficiency in Python and AI libraries such as PyTorch, TensorFlow, and Transformers (Hugging Face).
- Strong experience with LLMs, prompt engineering, and fine-tuning.
- Practical understanding of RAG systems using LangChain and vector databases (e.g., FAISS, Chroma, Pinecone).
- Hands-on experience in agentic AI frameworks (e.g., CrewAI, AutoGen, LangGraph, or LangChain Agents).
- Knowledge of tool integration, memory management, and multi-agent orchestration.
- Experience deploying AI models with FastAPI, Docker, Kubernetes, or cloud-native tools.
- Familiarity with MLOps pipelines, CI/CD automation, and monitoring frameworks.
- Exposure to Generative AI APIs such as OpenAI, Anthropic Claude, Google Gemini, or Azure OpenAI.
- Education: Bachelor's or Master's degree in Computer Science, Data Science, or Artificial Intelligence or similar technical discipline.
- Excellent communication, mentoring, and technical training skills.
- Proven experience conducting technical workshops, bootcamps, or corporate AI training programs preferred.
- Ready to deliver on-site and virtual training.
Preferred Skills/Attributes- Certifications in Machine Learning, Generative AI, or Cloud AI services.
- Experience developing autonomous AI agents and multi-agent ecosystems.
- Working knowledge of vector search optimization, knowledge graph integration, and RAG performance tuning.
- Understanding of AI ethics, bias mitigation, and responsible AI deployment.
- Enthusiasm for teaching and guiding professionals through hands-on AI and MLOps implementations.
