Systems Architect & Quant Engineer

I engineer autonomous systems and trading infrastructure.
Turning research code into production reality.Not just endpoints—but distributed adaptive intelligence.

I am Ayush Verma (ayushv). Principal Engineer architecting distributed inference clusters and high-frequency trading engines. I build the control planes, orchestration layers, and execution algorithms myself—no bloat, just significantly engineered systems.

ayushv quant dev ai dev machine learning engineer full stack dev Ayush Verma solo engineer principal engineer LangGraph expert trading infrastructure AutoGluon PyTorch SageMaker
ayushv is a quant dev, ai dev, and full stack dev. Ayush Verma portfolio for AI and trading.
PyTorch
TensorFlow
Keras
Scikit-learn
AutoGluon
Hugging Face Transformers
NVIDIA CUDA & NIM
LLMs & RAG
LightGBM
XGBoost
CatBoost
Random Forests
HMM (Hidden Markov Models)
Custom Gradient Boosting
AWS SageMaker
GCP Vertex AI
Azure ML
LangGraph
LangChain
LlamaIndex
FastAPI
Flask
Node.js
NestJS
Java / Spring Boot
PostgreSQL
TimescaleDB
Redis
SQLite
Kafka
NATS
WebSockets
Microservices
Supabase
Prometheus
Grafana
React
Next.js
Tailwind CSS
Framer Motion
Plotly Dash
Streamlit
UX/UI Design
MVP UI Systems
Docker
Kubernetes
Terraform
GitHub Actions
AWS
Azure
Railway
DigitalOcean
GCP
Replit
Environment Scaffolding
OpenAI Agents
n8n Workflows
Custom SDKs
Multi-tier Workflows
Automations
Scraping Pipelines
Deepgram
Simli
LiveKit
HeyGen
Nero AI Avatar Generator
Statistical Arbitrage
Backtesting Engines
ETF Analytics
Pine Script
Trading Automations
Risk & Monitoring
System Design
Architecture Blueprints
Multi-tier Workflows
MVP Specialization
Quick Startup Liftoff
Terminal / Shell
Web Scraping
Notion Certified Expert
SDK Design
ETL / CRUD Systems
PyTorch
TensorFlow
Keras
Scikit-learn
AutoGluon
Hugging Face Transformers
NVIDIA CUDA & NIM
LLMs & RAG
LightGBM
XGBoost
CatBoost
Random Forests
HMM (Hidden Markov Models)
Custom Gradient Boosting
AWS SageMaker
GCP Vertex AI
Azure ML
LangGraph
LangChain
LlamaIndex
FastAPI
Flask
Node.js
NestJS
Java / Spring Boot
PostgreSQL
TimescaleDB
Redis
SQLite
Kafka
NATS
WebSockets
Microservices
Supabase
Prometheus
Grafana
React
Next.js
Tailwind CSS
Framer Motion
Plotly Dash
Streamlit
UX/UI Design
MVP UI Systems
Docker
Kubernetes
Terraform
GitHub Actions
AWS
Azure
Railway
DigitalOcean
GCP
Replit
Environment Scaffolding
OpenAI Agents
n8n Workflows
Custom SDKs
Multi-tier Workflows
Automations
Scraping Pipelines
Deepgram
Simli
LiveKit
HeyGen
Nero AI Avatar Generator
Statistical Arbitrage
Backtesting Engines
ETF Analytics
Pine Script
Trading Automations
Risk & Monitoring
System Design
Architecture Blueprints
Multi-tier Workflows
MVP Specialization
Quick Startup Liftoff
Terminal / Shell
Web Scraping
Notion Certified Expert
SDK Design
ETL / CRUD Systems
PyTorch
TensorFlow
Keras
Scikit-learn
AutoGluon
Hugging Face Transformers
NVIDIA CUDA & NIM
LLMs & RAG
LightGBM
XGBoost
CatBoost
Random Forests
HMM (Hidden Markov Models)
Custom Gradient Boosting
AWS SageMaker
GCP Vertex AI
Azure ML
LangGraph
LangChain
LlamaIndex
FastAPI
Flask
Node.js
NestJS
Java / Spring Boot
PostgreSQL
TimescaleDB
Redis
SQLite
Kafka
NATS
WebSockets
Microservices
Supabase
Prometheus
Grafana
React
Next.js
Tailwind CSS
Framer Motion
Plotly Dash
Streamlit
UX/UI Design
MVP UI Systems
Docker
Kubernetes
Terraform
GitHub Actions
AWS
Azure
Railway
DigitalOcean
GCP
Replit
Environment Scaffolding
OpenAI Agents
n8n Workflows
Custom SDKs
Multi-tier Workflows
Automations
Scraping Pipelines
Deepgram
Simli
LiveKit
HeyGen
Nero AI Avatar Generator
Statistical Arbitrage
Backtesting Engines
ETF Analytics
Pine Script
Trading Automations
Risk & Monitoring
System Design
Architecture Blueprints
Multi-tier Workflows
MVP Specialization
Quick Startup Liftoff
Terminal / Shell
Web Scraping
Notion Certified Expert
SDK Design
ETL / CRUD Systems
PyTorch
TensorFlow
Keras
Scikit-learn
AutoGluon
Hugging Face Transformers
NVIDIA CUDA & NIM
LLMs & RAG
LightGBM
XGBoost
CatBoost
Random Forests
HMM (Hidden Markov Models)
Custom Gradient Boosting
AWS SageMaker
GCP Vertex AI
Azure ML
LangGraph
LangChain
LlamaIndex
FastAPI
Flask
Node.js
NestJS
Java / Spring Boot
PostgreSQL
TimescaleDB
Redis
SQLite
Kafka
NATS
WebSockets
Microservices
Supabase
Prometheus
Grafana
React
Next.js
Tailwind CSS
Framer Motion
Plotly Dash
Streamlit
UX/UI Design
MVP UI Systems
Docker
Kubernetes
Terraform
GitHub Actions
AWS
Azure
Railway
DigitalOcean
GCP
Replit
Environment Scaffolding
OpenAI Agents
n8n Workflows
Custom SDKs
Multi-tier Workflows
Automations
Scraping Pipelines
Deepgram
Simli
LiveKit
HeyGen
Nero AI Avatar Generator
Statistical Arbitrage
Backtesting Engines
ETF Analytics
Pine Script
Trading Automations
Risk & Monitoring
System Design
Architecture Blueprints
Multi-tier Workflows
MVP Specialization
Quick Startup Liftoff
Terminal / Shell
Web Scraping
Notion Certified Expert
SDK Design
ETL / CRUD Systems

ayushv Focus Areas

I specialize in building cutting-edge systems as a quant dev and ai dev.

AI Dev & Systems

As an ai dev and ML engineer, I build production-grade model pipelines, from auto-tuned boosting ensembles to custom deep learning architectures.

Stack: PyTorch, TensorFlow, AutoGluon, XGBoost/LightGBM/CatBoost, and deployed on AWS SageMaker, GCP Vertex AI, or Azure ML.

Voice + Multimodal AI

Real-time assistants with STT/TTS, camera/vision models, and tool execution for ops and research teams.

Streaming transcription, neural voice libraries, guardrails, and narrated transcripts for compliance reviews (ai dev).

Quant Dev & Trading Infra

Signals, pair trading engines, and telemetry-rich pipelines tuned for low latency and auditability.

Python + FastAPI services, Redis/PG for state, Prometheus dashboards, and LangGraph risk orchestrators (quant dev).

Agentic Workflows

Custom orchestration that coordinates multiple providers, humans-in-loop, and SaaS tooling.

LangGraph > LangChain for deterministic control, governance hooks, evaluation harnesses, and playbooks by ayushv.

Full Stack Dev & Dashboards

Executive-ready analytics for revenue, risk, or ops wrapped in delightful product UX.

Next.js frontends, supersonic charts, embedded auth, and automated QA as a full stack dev.

Data & Platform Ops

Foundational data ingestion, observability, and compliance rails for lean teams.

FastAPI + Redis, PostgreSQL/pgvector, tracing, and deployment patterns that stay sane on lean infra.

OSINT & Security

Ethical hacking and open-source intelligence pipelines for threat detection and data gathering.

Automated recon scanners, public data correlation, and security audit harnesses for deployed AI agents.

Featured Projects

A selection of recent projects I've worked on.

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

From ML pipelines to multi-tier automations.

I design and build systems that connect data, models, and workflows into practical tools — from quant research engines to multi-model AI platforms.

AI & Machine Learning

20 skills

Models, training, serving, and applied LLM work.

PyTorchTensorFlowKerasScikit-learnAutoGluonHugging Face TransformersNVIDIA CUDA & NIMLLMs & RAGLightGBMXGBoostCatBoostRandom ForestsHMM (Hidden Markov Models)Custom Gradient BoostingAWS SageMakerGCP Vertex AIAzure MLLangGraphLangChainLlamaIndex

Backend & Data

16 skills

APIs, data pipelines, and persistence.

FastAPIFlaskNode.jsNestJSJava / Spring BootPostgreSQLTimescaleDBRedisSQLiteKafkaNATSWebSocketsMicroservicesSupabasePrometheusGrafana

Frontend & UI

8 skills

Interfaces, dashboards, and interaction design.

ReactNext.jsTailwind CSSFramer MotionPlotly DashStreamlitUX/UI DesignMVP UI Systems

Infra & DevOps

11 skills

Environments, deployment, and reliability.

DockerKubernetesTerraformGitHub ActionsAWSAzureRailwayDigitalOceanGCPReplitEnvironment Scaffolding

Automation & Agents

11 skills

Multi-tier workflows and automation.

OpenAI Agentsn8n WorkflowsCustom SDKsMulti-tier WorkflowsAutomationsScraping PipelinesDeepgramSimliLiveKitHeyGenNero AI Avatar Generator

Trading & Quant

6 skills

Research systems and strategy tooling.

Statistical ArbitrageBacktesting EnginesETF AnalyticsPine ScriptTrading AutomationsRisk & Monitoring

Systems & Architecture

5 skills

Blueprinting and high-level thinking.

System DesignArchitecture BlueprintsMulti-tier WorkflowsMVP SpecializationQuick Startup Liftoff

General Engineering

5 skills

Everyday tools that keep things moving.

Terminal / ShellWeb ScrapingNotion Certified ExpertSDK DesignETL / CRUD Systems

One Lead, Scalable Support

I operate as a principal engineer — architecting, coding, and shipping the core myself. For specialist needs, I bring in a trusted network. You get solo-founder agility with full-agency capability.

AI Engineering & Market Insights

Technical deep-dives for the 2026 AI and Fintech landscape.

How to build a production-grade RAG pipeline in 2026?

Building a production-ready Retrieval-Augmented Generation (RAG) system today requires more than just a vector database. It demands enterprise-grade orchestration with tool like LangGraph, multi-stage retrieval (re-ranking), and robust evaluation harnesses to ensure deterministic outputs and prevent hallucinations.

What is the best tech stack for real-time AI avatars?

Real-time AI avatars require a low-latency bridge between STT, LLM inference, and TTS. We use Next.js for the interface, FastAPI for the backend, and specialized providers like Simli or Deepgram for sub-second lip-sync and voice response times.

How to optimize statistical arbitrage strategies for crypto?

Optimizing cointegration-based pairs trading in crypto involves high-frequency market data pipelines, event-driven architecture, and financial anomaly detection using LSTMs. Telemetry-rich pipelines tuned for low latency are critical for maintaining alpha.

Why choose a full-stack AI engineer for your MVP?

A full-stack AI engineer provides the agility of a solo founder with the technical depth of an entire team. By handling everything from Dockerized AI environments to serverless backend orchestration, you ensure a cohesive architecture that scales from day one.

Frequently Asked Questions

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.

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