// Import the rendercv function and all the refactored components
#import "@preview/rendercv:0.1.0": *

// Apply the rendercv template with custom configuration
#show: rendercv.with(
  name: "Aayush Bajaj",
  footer: context { [#emph[Aayush Bajaj -- #str(here().page())\/#str(counter(page).final().first())]] },
  top-note: [ #emph[Last updated in Jan 2026] ],
  locale-catalog-language: "en",
  page-size: "us-letter",
  page-top-margin: 1.3cm,
  page-bottom-margin: 1.3cm,
  page-left-margin: 1.2cm,
  page-right-margin: 1.2cm,
  page-show-footer: false,
  page-show-top-note: false,
  colors-body: rgb(0, 0, 0),
  colors-name: rgb(0, 0, 0),
  colors-headline: rgb(0, 0, 0),
  colors-connections: rgb(0, 0, 0),
  colors-section-titles: rgb(0, 0, 0),
  colors-links: rgb(0, 79, 144),
  colors-footer: rgb(128, 128, 128),
  colors-top-note: rgb(128, 128, 128),
  typography-line-spacing: 0.55em,
  typography-alignment: "justified",
  typography-date-and-location-column-alignment: right,
  typography-font-family-body: "New Computer Modern",
  typography-font-family-name: "New Computer Modern",
  typography-font-family-headline: "Source Sans 3",
  typography-font-family-connections: "New Computer Modern",
  typography-font-family-section-titles: "New Computer Modern",
  typography-font-size-body: 10pt,
  typography-font-size-name: 28pt,
  typography-font-size-headline: 10pt,
  typography-font-size-connections: 10pt,
  typography-font-size-section-titles: 1.3em,
  typography-small-caps-name: false,
  typography-small-caps-headline: false,
  typography-small-caps-connections: false,
  typography-small-caps-section-titles: false,
  typography-bold-name: true,
  typography-bold-headline: false,
  typography-bold-connections: false,
  typography-bold-section-titles: true,
  links-underline: true,
  links-show-external-link-icon: false,
  header-alignment: center,
  header-photo-width: 3.5cm,
  header-space-below-name: 0.3cm,
  header-space-below-headline: 0.7cm,
  header-space-below-connections: 0.3cm,
  header-connections-hyperlink: true,
  header-connections-show-icons: true,
  header-connections-display-urls-instead-of-usernames: true,
  header-connections-separator: "",
  header-connections-space-between-connections: 0.5cm,
  section-titles-type: "with_full_line",
  section-titles-line-thickness: 0.5pt,
  section-titles-space-above: 0.25cm,
  section-titles-space-below: 0.15cm,
  sections-allow-page-break: false,
  sections-space-between-text-based-entries: 0.3em,
  sections-space-between-regular-entries: 0.6em,
  entries-date-and-location-width: 4cm,
  entries-side-space: 0.15cm,
  entries-space-between-columns: 0.1cm,
  entries-allow-page-break: false,
  entries-short-second-row: false,
  entries-summary-space-left: 0cm,
  entries-summary-space-above: 0cm,
  entries-highlights-bullet:  "◦" ,
  entries-highlights-nested-bullet:  "-" ,
  entries-highlights-space-left: 0.3cm,
  entries-highlights-space-above: 0.1cm,
  entries-highlights-space-between-items: 0.05cm,
  entries-highlights-space-between-bullet-and-text: 0.4em,
  date: datetime(
    year: 2026,
    month: 1,
    day: 11,
  ),
)


= Aayush Bajaj

#connections(
  [#connection-with-icon("location-dot")[Sydney]],
  [#link("mailto:j@abaj.ai", icon: false, if-underline: false, if-color: false)[#connection-with-icon("envelope")[j\@abaj.ai]]],
  [#link("tel:+61-481-910-408", icon: false, if-underline: false, if-color: false)[#connection-with-icon("phone")[0481 910 408]]],
  [#link("https://abaj.ai/", icon: false, if-underline: false, if-color: false)[#connection-with-icon("link")[abaj.ai]]],
  [#link("https://github.com/abaj8494", icon: false, if-underline: false, if-color: false)[#connection-with-icon("github")[github.com\/abaj8494]]],
  [#link("https://linkedin.com/in/abaj", icon: false, if-underline: false, if-color: false)[#connection-with-icon("linkedin")[linkedin.com\/in\/abaj]]],
)


== Education

#education-entry(
  [
    #strong[University of New South Wales]

    #emph[Master of Statistics]

  ],
  [
    #emph[Sydney, Australia]

    #emph[Feb 2027 – Dec 2029]

  ],
  main-column-second-row: [
  ],
)

#education-entry(
  [
    #strong[University of New South Wales]

    #emph[Bachelor of Computer Science (AI), Minor Mathematics]

  ],
  [
    #emph[Sydney, Australia]

    #emph[Feb 2021 – Sept 2025]

  ],
  main-column-second-row: [
  ],
)

== Skills

#strong[Deep Learning:] PyTorch, TensorFlow, HuggingFace Transformers, nnU-Net, U-Net, CNNs, LLMs, RAG

#strong[ML Engineering:] Python, CUDA, HPC (H200\/A200), Distributed Training, Model Optimisation, Hyperparameter Tuning

#strong[Computer Vision:] Semantic Segmentation, 3D Medical Imaging, Data Augmentation, Preprocessing Pipelines

#strong[LLM & GenAI:] LangChain, LangGraph, LlamaIndex, smolagents, OpenAI API, Prompt Engineering, Fine-tuning, RAG

#strong[MLOps & Cloud:] Docker, AWS, Azure, Git, Experiment Tracking, Model Deployment

== Projects

#regular-entry(
  [
    #strong[#link("https://abaj.ai/projects/dl/kits19")[Biomedical Semantic Segmentation]]

  ],
  [
    #emph[KiTS19]

  ],
  main-column-second-row: [
    #summary[Production-grade kidney and tumour segmentation from 3D CT scans on official KiTS19 leaderboard.]

    - Achieved 0.9129 Dice score (ranked \#57 globally) by implementing nnU-Net with systematic hyperparameter optimisation on H200 GPUs.

    - Reproduced state-of-the-art 3D medical imaging pipeline end-to-end; published technical report with ablation studies comparing 2D vs 3D architectures.

  ],
)

  #regular-entry(
  [
    #strong[Full-Stack LLM Chat Application]

  ],
  [
  ],
  main-column-second-row: [
    #summary[Production-ready conversational AI with RAG pipeline, streaming responses, and persistent context.]

    - Architected full-stack chat application using TypeScript, Bun, Svelte, and Prisma ORM with PostgreSQL; integrated OpenAI GPT API with streaming.

    - Implemented security middleware with rate limiting, response caching, and structured logging; validated with Zod schemas and comprehensive test suite.

  ],
)

#regular-entry(
  [
    #strong[#link("https://github.com/abaj8494/10khrs-ai-ml-dl")[Neural Networks: Zero to Hero]]

  ],
  [
    #emph[Karpathy]

  ],
  main-column-second-row: [
    #summary[From-scratch implementations of neural networks culminating in a GPT language model.]

    - Built micrograd (autograd engine), makemore (character-level LM), and nanoGPT following Andrej Karpathy's curriculum.

    - Implemented backpropagation, attention mechanisms, and transformer architecture from first principles in pure Python\/PyTorch.

  ],
)

#regular-entry(
  [
    #strong[#link("https://abaj.ai/doc/publications/dead-tree-seg.pdf")[Dead Tree Segmentation]]

  ],
  [
    #emph[Kaggle]

  ],
  main-column-second-row: [
    #summary[Semantic segmentation of live\/dead trees from satellite imagery with severe class imbalance.]

    - Achieved 0.71 IoU on 97\% imbalanced dataset by implementing U-Net from scratch (31M parameters) with Dice + Focal loss combination.

    - Parallelised training pipeline on CUDA-enabled HPC; applied morphological post-processing for boundary refinement.

  ],
)

#regular-entry(
  [
    #strong[#link("https://abaj.ai")[Augmenting Infrastructure]]

  ],
  [
    #emph[Monorepo]

  ],
  main-column-second-row: [
    #summary[Technical knowledge platform with ML\/AI research notes, graduate-level mathematics, tutorials, and paper summaries.]

    - Architected full-stack content platform with Hugo static generation, Emacs org-mode authoring, and bidirectional linking across 1,073+ pages.

    - Built literate programming environment with org-babel for executable Python\/Julia notebooks, replicating Jupyter workflows with version control.

  ],
)

== Certificates

#regular-entry(
  [
    #strong[AWS Solutions Architect (SAA-C03)]

  ],
  [
    #emph[Jan 2026 – Feb 2026]

  ],
  main-column-second-row: [
    #emph[Amazon Web Services]

  ],
)

#regular-entry(
  [
    #strong[AI Agents Course]

  ],
  [
    #emph[Jan 2026 – Jan 2026]

  ],
  main-column-second-row: [
    #emph[Hugging Face]

  ],
)