I engineer autonomous systems and trading infrastructure.
Turning research code into production reality.
Not just endpoints—but distributed adaptive intelligence.
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)
Event-driven market scanner processing 500+ ticks/s

Regime-Switching Scalper
Production 24/7 multi-asset scalper with runtime market regime detection
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.
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()789def 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()131415# orchestrator: exact-hash → trigram → fallback16async def check_duplicate(session, campaign_id, content, platform, angle, threshold=0.7):17 content_hash = compute_content_hash(content, platform, angle)1819 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}2223 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)2627 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).
1type JsonLdProps = {2 data: Record<string, unknown> | Array<Record<string, unknown>>;3};45export 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 });1112 const json =13 graphNodes.length === 114 ? { "@context": "https://schema.org", ...graphNodes[0] }15 : { "@context": "https://schema.org", "@graph": graphNodes };1617 return (18 <script19 type="application/ld+json"20 dangerouslySetInnerHTML={{ __html: JSON.stringify(json) }}21 />22 );23}
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 skillsModels, training, serving, and applied LLM work.
Backend & Data
16 skillsAPIs, data pipelines, and persistence.
Frontend & UI
8 skillsInterfaces, dashboards, and interaction design.
Infra & DevOps
11 skillsEnvironments, deployment, and reliability.
Automation & Agents
11 skillsMulti-tier workflows and automation.
Trading & Quant
6 skillsResearch systems and strategy tooling.
Systems & Architecture
5 skillsBlueprinting and high-level thinking.
General Engineering
5 skillsEveryday tools that keep things moving.
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.