Job Title

About the RoleWe are looking for a rare blend of deep technical knowledge and the ability to communicate it well. This role sits at the intersection of an ML Engineer and a Deep Learning Data Scientist — someone who understands the mathematics and algorithmics behind modern deep learning systems, pipelines, agents and can work hands-on with code, cloud infrastructure, and can translate that expertise into structured, high-quality learning experiences.What You'll DoYou must have a state of AI understanding of Deep Machine Learning, MLOps, LLMs, NLP Foundations, Agentic AI, and Generative AI.Mathematically deduce Deep Learning Algorithms into simple knowledge bytes and lessons. Build, train, and deploy end-to-end ML pipelines on AWS, capable of demonstrating real-world deep learning workflows to users. Work with and explain the algorithmics of modern deep learning architectures — Transformers, LLMs, GANs, and recent advances including RAG, LoRA, AI Agents, and Agentic AI systems.Curate and validate technical content for accuracy, depth, and pedagogical clarity across beginner, intermediate, and advanced learner levels.Develop and maintain code repositories on GitHub that serve as reference implementations and lab environments for course participants.Review, sequence, and structure curriculum modules to ensure a coherent and well-scaffolded learner journey.Stay current with the latest research and tooling in deep learning, and continuously incorporate relevant advancements into course content.Regularly guide learners through complex technical concepts through regular live or recorded sessions, making sophisticated ideas approachable without oversimplifying them.What We're Looking ForMust-havesDeep expertise in the mathematical algorithmics of deep learning — including the mechanics of neural architectures, optimisation theory, attention mechanisms, and probabilistic foundations.Strong command of modern deep learning systems: Transformers, LLMs, GANs, diffusion models, RAG pipelines, LoRA fine-tuning, and agentic frameworks.Advanced Python programming skills — clean, well-documented, production-aware code.Hands-on experience with at least one major cloud platform (AWS preferred; GCP or Azure also considered), including compute, storage, and ML service offerings.Proficiency with GitHub for version control, collaboration, and repository management.Comfort working in Linux environments.Excellent written and spoken English — able to explain complex technical ideas with precision and clarity to a range of audiences.A genuine passion for teaching and helping others build deep technical understanding.Nice-to-havesHands-on experience with frameworks such as Hugging Face Transformers, LangChain, LlamaIndex, PyTorch, or JAX.Familiarity with MLOps practices and tools — experiment tracking, model versioning, CI/CD for ML, and pipeline orchestration.Prior experience creating technical content — tutorials, notebooks, documentation, course material, or technical blog posts.Exposure to instructional design principles or experience in teaching, tutoring, or mentoring in a technical subject.Awareness of leading AI learning platforms and curricula (fast.ai, d2l.ai, DeepLearning.AI, Hugging Face courses, etc.).Tech Stack Advanced Python Programming AWSDockerGitHubLinux/ Shell Scripting MLFLowWhat We OfferA central role in building deep learning curricula used by real learners and professionals.Mentorship from senior AI researchers, engineers, and learning designers.Hands-on access to cloud compute resources for experimentation and content development.Opportunity to publish technical content, maintain open-source repositories, and build a visible body of work.Flexible hours and an async-first work culture.Pre-placement offer (PPO) consideration for outstanding performers.Paid a fixed monthly stipend and Performance based incentive