AI & Data Infrastructure Engineer

I build production systems for AI automation, data pipelines, and analytics.
Backend architecture · LLM orchestration · Scalable data infrastructure.

I'm Ayush Verma — a software engineer with ~3 years of hands-on experience building end-to-end AI and data systems. Everything I ship is real, Dockerized, and deployed.

Explore selected systems and case studies below.

LangGraph
LangChain
VertexAI
RAG / FAISS / pgvector
OSS LLMs (Ollama, vLLM)
Whisper (STT)
HMM & Regime Detection
Scikit-learn
Pandas / NumPy
Hugging Face
Python
FastAPI
PostgreSQL
TimescaleDB
Redis
SQLAlchemy
Pydantic
WebSockets
Prometheus
React
Next.js
TypeScript
Tailwind CSS
Framer Motion
Three.js
React Three Fiber
TradingView Charts
Docker
GitHub Actions
GCP / Azure
Cloud Computing
DigitalOcean / Railway
Linux / Shell Scripting
Process Automation
Webflow
Bubble
Airtable
Zapier / Make
Softr / Glide
Statistical Arbitrage
Walk-Forward Backtesting
Binance WebSocket API
Cointegration Analysis
Risk & Monitoring
Event-Driven Architecture
LangGraph
LangChain
VertexAI
RAG / FAISS / pgvector
OSS LLMs (Ollama, vLLM)
Whisper (STT)
HMM & Regime Detection
Scikit-learn
Pandas / NumPy
Hugging Face
Python
FastAPI
PostgreSQL
TimescaleDB
Redis
SQLAlchemy
Pydantic
WebSockets
Prometheus
React
Next.js
TypeScript
Tailwind CSS
Framer Motion
Three.js
React Three Fiber
TradingView Charts
Docker
GitHub Actions
GCP / Azure
Cloud Computing
DigitalOcean / Railway
Linux / Shell Scripting
Process Automation
Webflow
Bubble
Airtable
Zapier / Make
Softr / Glide
Statistical Arbitrage
Walk-Forward Backtesting
Binance WebSocket API
Cointegration Analysis
Risk & Monitoring
Event-Driven Architecture
LangGraph
LangChain
VertexAI
RAG / FAISS / pgvector
OSS LLMs (Ollama, vLLM)
Whisper (STT)
HMM & Regime Detection
Scikit-learn
Pandas / NumPy
Hugging Face
Python
FastAPI
PostgreSQL
TimescaleDB
Redis
SQLAlchemy
Pydantic
WebSockets
Prometheus
React
Next.js
TypeScript
Tailwind CSS
Framer Motion
Three.js
React Three Fiber
TradingView Charts
Docker
GitHub Actions
GCP / Azure
Cloud Computing
DigitalOcean / Railway
Linux / Shell Scripting
Process Automation
Webflow
Bubble
Airtable
Zapier / Make
Softr / Glide
Statistical Arbitrage
Walk-Forward Backtesting
Binance WebSocket API
Cointegration Analysis
Risk & Monitoring
Event-Driven Architecture
LangGraph
LangChain
VertexAI
RAG / FAISS / pgvector
OSS LLMs (Ollama, vLLM)
Whisper (STT)
HMM & Regime Detection
Scikit-learn
Pandas / NumPy
Hugging Face
Python
FastAPI
PostgreSQL
TimescaleDB
Redis
SQLAlchemy
Pydantic
WebSockets
Prometheus
React
Next.js
TypeScript
Tailwind CSS
Framer Motion
Three.js
React Three Fiber
TradingView Charts
Docker
GitHub Actions
GCP / Azure
Cloud Computing
DigitalOcean / Railway
Linux / Shell Scripting
Process Automation
Webflow
Bubble
Airtable
Zapier / Make
Softr / Glide
Statistical Arbitrage
Walk-Forward Backtesting
Binance WebSocket API
Cointegration Analysis
Risk & Monitoring
Event-Driven Architecture

Selected Projects

Real systems, shipped and deployed.

Divergence Detector (Advanced)
View Details

Divergence Detector (Advanced)

Benchmark
Live

Event-driven market scanner processing 500+ ticks/s

trading
Python / FastAPI
React / Tailwind
+4
Regime-Switching Scalper
View Details

Regime-Switching Scalper

Benchmark
Live

Production 24/7 multi-asset scalper with runtime market regime detection

trading
Python
FastAPI (Async)
+5

Code Snippets

Small excerpts from real systems — the kind of details that make products reliable.

Stable content hashing + near-duplicate detection

A layered dedupe strategy: stable hash → pg_trgm similarity → pure-Python fallback.

Python
1# normalize + stable hash (accounts for platform & angle)
2def normalize_content(content: str) -> str:
3 normalized = content.lower()
4 normalized = re.sub(r"\s+", " ", normalized)
5 normalized = re.sub(r"[^\w\s]", "", normalized)
6 return normalized.strip()
7
8
9def compute_content_hash(content: str, platform: str, angle: str) -> str:
10 normalized = normalize_content(content)
11 hash_input = f"{normalized}|{platform.lower()}|{angle.lower()}"
12 return hashlib.sha256(hash_input.encode("utf-8")).hexdigest()
13
14
15# orchestrator: exact-hash → trigram → fallback
16async def check_duplicate(session, campaign_id, content, platform, angle, threshold=0.7):
17 content_hash = compute_content_hash(content, platform, angle)
18
19 exact = await check_exact_duplicate(session, campaign_id, content_hash)
20 if exact:
21 return {"is_duplicate": True, "type": "exact", "existing_post_id": exact.id}
22
23 near = await check_near_duplicate_trgm(session, campaign_id, content, threshold)
24 if not near:
25 near = await check_near_duplicate_fallback(session, campaign_id, content, threshold)
26
27 return {"is_duplicate": bool(near), "type": "near" if near else None, "content_hash": content_hash}

JSON-LD graph injector

Keeps schema composable while emitting one canonical @context (and @graph when needed).

TypeScript / React
1type JsonLdProps = {
2 data: Record<string, unknown> | Array<Record<string, unknown>>;
3};
4
5export function JsonLd({ data }: JsonLdProps) {
6 const payload = Array.isArray(data) ? data : [data];
7 const graphNodes = payload.map((node) => {
8 const { "@context": _ctx, ...rest } = node as Record<string, unknown>;
9 return rest;
10 });
11
12 const json =
13 graphNodes.length === 1
14 ? { "@context": "https://schema.org", ...graphNodes[0] }
15 : { "@context": "https://schema.org", "@graph": graphNodes };
16
17 return (
18 <script
19 type="application/ld+json"
20 dangerouslySetInnerHTML={{ __html: JSON.stringify(json) }}
21 />
22 );
23}
Skills & Capabilities

Stack I actually use.

These are the tools in the projects above — Python, FastAPI, LangGraph, React/Next.js, PostgreSQL, Redis, Docker, and the surrounding ecosystem.

AI & LLM

10 skills

LLM orchestration, RAG pipelines, and applied ML.

LangGraphLangChainVertexAIRAG / FAISS / pgvectorOSS LLMs (Ollama, vLLM)Whisper (STT)HMM & Regime DetectionScikit-learnPandas / NumPyHugging Face

Backend & Data

9 skills

APIs, async pipelines, and data persistence.

PythonFastAPIPostgreSQLTimescaleDBRedisSQLAlchemyPydanticWebSocketsPrometheus

Frontend

8 skills

Production UIs and interactive dashboards.

ReactNext.jsTypeScriptTailwind CSSFramer MotionThree.jsReact Three FiberTradingView Charts

Infra & Deployment

7 skills

End-to-end deployment and cloud infrastructure.

DockerGitHub ActionsGCP / AzureCloud ComputingDigitalOcean / RailwayLinux / Shell ScriptingProcess Automation

Low-Code & Automation

5 skills

Rapid delivery with robust integrations.

WebflowBubbleAirtableZapier / MakeSoftr / Glide

Quant & Data Systems

6 skills

Market data pipelines and strategy tooling.

Statistical ArbitrageWalk-Forward BacktestingBinance WebSocket APICointegration AnalysisRisk & MonitoringEvent-Driven Architecture

How I Work

I handle architecture, backend, and delivery myself — end to end, Dockerized, and deployed. For larger scopes, I bring in people I've worked with before. You deal with one person throughout.

Frequently Asked Questions

Everything I ship is real, Dockerized, and deployed end-to-end. Common questions about how I work and what to expect.

Let's Build Something Great

I'm always interested in working on exciting projects and collaborations.

Chat on WhatsApp