About Me
I'm an AI engineer drawn to the unglamorous half of applied AI: the data pipelines that decide whether a model is actually useful. Across two years at Talentship.io and Octonomy.ai I built preprocessing, text chunking, and vector database ingestion pipelines, and tuned retrieval and embedding strategies to make semantic search return the right thing.
I joined during the early-stage generative AI and RAG research that became the Octonomy product. I'm now completing a Master of Artificial Intelligence at Victoria University of Wellington in New Zealand.
Alongside that I ship small tools and write about what I learn: fine-tuning on Apple Silicon, PDF extraction, agentic workflows.
- Languages
- PythonTypeScriptLua
- AI & ML
- LangChainPyTorchRAGVector DatabasesLLMs
- Build
- FastAPINext.jsStreamlitGradio
Mount Victoria lookout
Wellington, New Zealand
Experience & Education
Victoria University of Wellington
Jun 2026 - PresentMaster of Artificial Intelligence
Wellington, New Zealand
Octonomy.ai
Feb 2024 - Jun 2026Associate AI Data Engineer
- Designed and implemented data preprocessing, text chunking, and vector database ingestion pipelines using advanced embedding models.
- Optimized RAG pipelines and embedding strategies to improve semantic search performance.
Talentship.io
Feb 2024 - Jun 2026Associate AI Data Engineer
- Contributed to the core research team during early-stage development of the Octonomy product.
- Worked across generative AI and RAG research, LangChain, LLM-based systems, and AI pipeline design.
Sri Ramachandra Engineering and Technology
2020 - 2024B.Tech, Computer Science and Engineering (AI & ML)
GPA 8.11 / 10
Writing
All postsBuilding an AI Music Recommendation System: An Idea I Decided Not to Pursue
Discover my exploration of an AI music recommendation system, including hybrid machine learning, music datasets, data licensing challenges, and why I paused the project.
AI for Medical Image Analysis: Deep Learning Methods for Disease Detection
Discover how AI is revolutionizing medical imaging through deep learning. Learn how modern models detect diseases from CT scans, retinal images, and X-rays while exploring their strengths, limitations, and future potential.
How Agentic AI Is Changing Enterprise Workflows in 2025
A quick look at how agentic AI is transforming enterprise workflows in 2025, from automating complex cloud operations to streamlining cross-team tasks, boosting customer experience and redefining how humans collaborate with AI. Learn why these autonomous systems are becoming essential for modern businesses.
Let's Create Together
Have an AI project or collaboration in mind? I'd love to hear about it. Reach out and let's build something amazing.