Rajeev leads curriculum and technical direction at Deepskilling, with a focus on artefacts employers can inspect: evaluation harnesses, cloud architectures, and production-shaped capstones.
His teaching and writing cover retrieval-augmented generation, parameter-efficient fine-tuning, multi-cloud delivery, and interview-grade system design for ML and platform roles.
He works with cohort learners and enterprise workshop teams to turn proprietary use cases into reproducible engineering patterns—and, when novel, publishable case studies.
Expertise
- Generative AI & RAG systems
- LLM fine-tuning (LoRA / QLoRA)
- AWS & multi-cloud architecture
- System design for ML interviews
- Capstone & workshop facilitation