01
AI Disciplines
The problem spaces I design and ship systems in.
- Generative AI
- Computer Vision
- Natural Language Processing
- Deep Learning
- Machine Learning
- Retrieval-Augmented Generation
- Agentic AI
[ 04 / Skills ]
The toolkit, from tensor to endpoint
Grouped the way the work actually flows: pick the right discipline, model it, serve it, store it, and keep the GPU bill honest.
02
Frameworks & Libraries
Daily drivers for modelling, vision and orchestration.
- PyTorch
- TensorFlow
- OpenCV
- LangChain
- Hugging Face
- NeMo Guardrails
03
Serving & APIs
Turning models into reliable, documented services.
- FastAPI
- LitServe
- Docker
- Model Deployment
- REST APIs
04
Data & Storage
Retrieval, caching and persistence for AI systems.
- SQL
- Qdrant
- Weaviate
- Redis
- Vector Databases
05
Cloud & Compute
Provisioning and managing GPU workloads cost-effectively.
- Google Cloud Platform
- VastAI
- GPU Cost Optimisation
06
Engineering Practice
How the work stays maintainable and on schedule.
- Python
- Git
- Agile
- PEP8 Standards
- Data Structures
- Trivy
- VS Code
- Cursor
[ Beyond the stack ]
- Communication
- Team Work
- Critical Thinking
- Adaptability
- Time Management
- Research Documentation