build · ship · iterate

Ayaan Khan

ai engineer · builder

Open to work

Chicago, IL [email protected]

GitHub ↗ LinkedIn ↗

local 06:14 PM CST

./me.jpeg

activity

building: VeritasLayer (building),TrueTone (building),This site
learning: Agentic workflows in construction and real estate.
watching: The Bear (Show, 2026) · previously Beef, Succession
reading: Attention Is All You Need (Vaswani et al.)
hardware: MacBook Pro M4 Pro · iPhone 17 Pro · Samsung Z Fold 7
software: VS Code · Claude · Codex · Gemini · Ghostty
supporting: FC Barcelona & Argentina

Recently played

// 02 — about

About Me

AI engineer building production LLM systems — RAG pipelines, semantic search, multi-agent workflows. Originally from New Delhi, based in Chicago. Outside the terminal, I'm an avid football fan (the real kind), play guitar, and spend too much time thinking about music and art. I think good taste is a technical skill.

// where I'm headed

After graduation in May 2026, I want to join a team where I can ship AI systems that actually matter — whether that is at a fast-moving startup or a company building infrastructure for the next wave of intelligent software. Longer term, I want to build products of my own at the intersection of AI and systems design, and eventually contribute to research that makes LLM pipelines more reliable and interpretable.

education

Degree: B.S. in Artificial Intelligence
Minor: Minor in Architecture
University: Illinois Institute of Technology
Completion: May 2026

// coursework

01 DSA02 AI03 ML04 NLP05 DBMS06 Assembly07 Data Mining08 Discrete Math09 Linear Algebra10 Probability11 Statistics12 OOP

// 03 — experience

Experience

D.A. Syntec Ltd. · Chicago, Illinois · January 2026 – May 2026

AI & Digital Developer (Returning Intern)

1. Built and deployed the production RAG retrieval layer over firm documents (chunking, embedding, indexing with ChromaDB) — now the shared knowledge backbone for the SyntecAI suite, used by internal teams to answer project queries with cited sources instead of digging through file shares.
2. Built the CCN (Coding, Classification & Naming) Agent: a GPT-4o function-calling agent that looks up and edits firm classification codes in natural language, backed by SQLite with a synced ChromaDB index. Writes are atomic, with confirmation gates on every destructive edit.

atomic cross-store writes

D.A. Syntec Ltd. · Chicago, Illinois · May 2025 – December 2025

AI & Digital Developer (Intern)

1. Built a custom RAG pipeline using OpenAI and ChromaDB (chunking, embedding, indexing firm documents) for natural language retrieval — the technical foundation for the later production deployment above.

foundation for the production RAG layer

Volunteers.Covihelp · Remote (India) · May 2021 – July 2021

Co-Founder

1. Created and managed a 24/7 helpline during India's second COVID wave, connecting thousands of patients with critical resources like oxygen, beds, and medicines.

Excelerate (Globalshala) · New Delhi, India · June 2023 – July 2023

Project Manager

1. Led a global team to organize an academic event with a $30,000 budget, managing documentation, risk assessment, and external outsourcing.

// 04 — projects

Projects

↳ select a row for detail

// earlier work

// 05 — systems

Systems Thinking

01
Ingestion & preprocessing
Clean, chunk, and index messy docs so retrieval starts from ground truth
02
Hybrid retrieval + reranking
Vector search alone hallucinates on structured data — combine with lexical matching and rerank
03
Agentic workflows & guardrails
Function-calling agents with confirmation gates before anything destructive touches production
04
Structured outputs & validation
Every LLM response parsed through Pydantic schemas — if it doesn't conform, it doesn't ship
05
Latency/cost routing
LiteLLM across models, Redis caching, and fallback LLMs — not every query needs GPT-4o
06
Observability & failure modes
Inngest job tracing, eval harnesses — catch breaks before users do