About Me
I build production systems for AI automation, data pipelines, and backend infrastructure. Everything I ship is real, Dockerized, and deployed end-to-end. I'm Ayush Verma — about 3 years into building real systems across AI, data, and backend engineering. I handle architecture, core logic, and delivery myself, and bring in trusted collaborators only when the scope genuinely calls for it.
Background
I came to software through curiosity about how systems work at a low level. That mindset — finding overhead, reducing it, understanding constraints — shapes how I approach every build.
AI came before deep coding for me. I started with models and worked backwards into the engineering that makes them reliable in production: backends, data pipelines, evaluation, deployment. I use LLMs to accelerate drafting and cross-check designs, but every critical path gets reviewed, tested, and verified before anything goes live.
I grew up around a family business and learned early that the gap between success and failure isn't just the product — it's timing and data. That instinct shapes how I think about building systems that produce real outcomes.
Getting the GitHub Student Developer Pack early on was a catalyst — access to real infrastructure (DigitalOcean, JetBrains) let me move from local experiments to deployed production systems fast.
Engineering Case Stories
The Latency Obsession
From RC Toys to HFT
It started with tearing down PlayStations, Xbox consoles, laptops, CPUs, and tuning RC hardware. Around ages 12-14, I was building RC boats and measuring input-to-reaction lag before I had a name for it. That obsession never left. Today it shows up as rewriting hotspots in Rust and optimizing rendering and inference paths until systems feel instant.
Determinism in Chaos
Orchestrating AI
AI is inherently probabilistic; engineering must be deterministic. My approach to AI isn't just 'prompt engineering'—it's building rigid evaluation harnesses, guardrails, and state machines (LangGraph) that force these chaotic models to behave reliably. I treat LLMs like untrusted microservices, not magic boxes.
The Family Business
Pragmatism & Cycles
Growing up in a jewellery business, I learned that inventory is a liability until it's sold. I watched metal prices fluctuate and understood that 'value' is distinct from 'price'. This grounded my engineering in extreme pragmatism. I don't build tech for tech's sake; I build it to capture value, hedge risk, and solve the specific business problem at hand.
What I Build
AI Automation & LLM Systems
LangGraph agents, RAG pipelines, and LLM integrations wired into real backend systems — not wrappers.
Data Pipelines & Analytics
Event-driven ingestion, time-series storage (TimescaleDB/Redis), and analytics backends built on FastAPI and PostgreSQL.
Quant & Trading Infrastructure
Statistical arbitrage engines, market data pipelines, walk-forward backtesting, and real-time signal dashboards.
Full-Stack Products
Next.js frontends with production backends — deployed, Dockerized, and reliable.
How I Plan, Execute, and Ship
I stay crystal clear from day 1 through delivery: scope, risks, tradeoffs, milestones, and acceptance criteria are explicit across the full SDLC.
Phase 1
Plan
Map the actual business need, constraints, and success metrics before a line of code ships.
Product briefs, architecture spikes, and technical risk kill-lists
- Stakeholder interviews + async Loom walkthroughs
- System design + data flow diagrams
- Backlog + cutlines agreed before build starts
Phase 2
Execute
Build the core loops with instrumentation, tests, and documentation baked in.
Regular drops, transparent boards, and measurable progress
- Pairing on hairy subsystems when needed
- Framer Motion / Tailwind UI polish alongside backend work
- Observability + eval harnesses land before demo day
Phase 3
Ship
Tighten reliability, harden infra, and transfer knowledge so you can run it without me.
Handover playbooks, on-call notes, and optional launch support
- Load tests + failure plan captured in docs
- Recording Looms for every critical workflow
- Post-launch tweaks scoped with the same rigor
My Approach
System Design
I focus on designing scalable, maintainable systems that can handle real-world complexity and growth.
User Experience
I build intuitive interfaces that make complex systems accessible and enjoyable to use.
Performance First
I optimize for speed and efficiency without compromising on code quality or readability.
Skills & Stack
This reflects what I actually use in the projects on this site — Python, FastAPI, LangGraph, PostgreSQL/TimescaleDB, Redis, React/Next.js, Docker, and the cloud platforms I've deployed on.
AI & LLM
LLM orchestration, RAG pipelines, and applied ML.
Backend & Data
APIs, async pipelines, and data persistence.
Frontend
Production UIs and interactive dashboards.
Infra & Deployment
End-to-end deployment and cloud infrastructure.
Low-Code & Automation
Rapid delivery with robust integrations.
Quant & Data Systems
Market data pipelines and strategy tooling.
Engagement FAQ
Common questions about hiring me and my team.
Let's Build Something Great
I'm always interested in working on exciting projects and collaborations.