Website:
waltcorp.io
Job details:
About the Role
We 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 Do
- You 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 For
Must-haves
- Deep 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-haves
- Hands-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
- AWS
- Docker
- GitHub
- Linux/ Shell Scripting
- MLFLow
What We Offer
- A 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
Click on Apply to know more.