I have worked in enterprise architecture for twenty years and I have built and sold seven companies along the way. I still write production code most days. Today I do two things. I work as a forward deployed engineer: I go inside a company, build the AI system it needs with its real data, ship it into the systems its staff already use, and hand it over. And I act as a fractional Chief AI Officer for companies that need someone to own the AI decisions without a full-time hire. I am not an agency, and I do not specialize in one industry.
The short version
- Role
- Agentic AI architect, forward deployed engineer, fractional Chief AI Officer or CTO
- Experience
- Twenty years in enterprise architecture. Seven companies built and sold.
- Last year
- 8,202 GitHub contributions in the twelve months to 14 September 2026, across retail, trading, security, video, robotics and CPU inference. The homepage pulls that number live.
- Builds
- AI agents, multi-agent orchestration, agentic workflows, fine-tuned small language models, low-latency trading systems
- Languages
- TypeScript, Python, Rust, Solidity
- AI stack
- Claude Agent SDK, MCP servers, DSPy, GEPA, RAG, LLM fine-tuning, LLM-as-a-judge evaluation
- Platform
- Cloudflare Workers, D1, R2, Docker, Ansible, PostgreSQL, ClickHouse, Redis
- Also
- Robotics simulation and sim2real, AI safety and prompt injection detection, on-chain settlement and MEV
- Works
- Remote, worldwide. One or two engagements at a time.
- Find me
- GitHub · LinkedIn · parun@apeg.dev
What I do now
Most AI projects inside companies stop at a pilot. The demo works, the vendor leaves, and nobody inside owns it, so it is never used with real data or real customers. The work I do is the part after the demo. I work inside the company, usually remotely, with access to its repositories, its accounts and its people. I write down the decisions that are the owner's and not mine. Then I build, in weeks, with the company's real data, and every week something ships that a real person can use, such as a page, an email or a report. When it runs, I write a handover document and leave. The how it runs page walks through one engagement week by week, from the records.
The fractional Chief AI Officer work is the other half. Some companies do not need a system built yet. They need someone who has done this to decide what to build, what to buy, what to leave alone, and who will own it. I take that on for a few days a month. It uses the same judgment as the engineering work, but before any engineering starts. I also work as a fractional CTO on the same terms. I have been a co-founder and CTO seven times, and some companies need a CTO before it makes sense to decide anything about AI.
Both are AI work. Almost everything I have shipped in the last two years has a language model somewhere in it: reading documents, planning steps, writing code, grading its own output. I use AI coding agents heavily to build, and I enforce quality with the compiler and the test suite rather than with a written guideline, because an agent cannot ship code that the compiler refuses.
The last twelve months
The number I point people to is on my GitHub account: 8,202 contributions in the year to 14 September 2026. It covers many unrelated domains, one after another and sometimes in parallel. I list them here because I would rather show the work than describe it. Client names, product names and cities are left out on purpose; the work was done inside other people's companies.
| When | What was built | What I owned |
|---|---|---|
| Sep 2024 to Mar 2026 | The operating platform for a retail chain: purchase orders, cost of goods, debit notes, stock transfers between stores, seasons and size sets, monthly targets, role-based access, and a point-of-sale storefront. Integrated into the point-of-sale, accounting and shipping systems the business already ran. Nineteen months, a team of seven, released on a version cadence. | The architecture. The design of more than 45 domain modules. The split between the transactional path and a separate analytics path so reporting never slowed down the checkout registers. The integrations into the customer's existing systems. The team. |
| Jun 2025 to Jan 2026 | A quantitative trading platform in Rust: a five-stage signal pipeline, real-time risk management, multiple exchanges, each signal running in its own isolated sub-account, a hot streaming path and a cold analytics store. | All of it, alone. Including the production defects, fixed and numbered: deadlocks, an order race, false position closure on reconnect, ghost signals. |
| Jan to Mar 2026 | On-chain systems: a real-time liquidation monitor over WebSocket streams with no polling, and an order workflow that goes from an incoming webhook through nine checks to a placed and verified order in about 210 milliseconds, measured. | All of it, alone. |
| Mar 2026 | Security: a pay-per-call firewall that protects AI agents from prompt injection, hallucinated output and stuck loops, and a smart contract audit pipeline whose prompts were rewritten by an optimizer with a language model as the judge. Real bounty submissions came out of it. | All of it, alone. |
| Jan to Sep 2026 | A video production and distribution product, built inside a founder's company: transcription, an AI planner that writes the motion graphics code itself, a render pipeline, and distribution to platforms that each have their own rules. This is the engagement the how it runs page is drawn from. | Every line of code. The decisions file. The weekly cadence. Six rounds of review corrections. The handover. |
| May to Jun 2026 | Robotics: a chat-driven operator console where a person types a task and a simulated robot carries it out, and the bridge between a physics simulator and the open robot-learning stack, which did not exist before. Published on PyPI under my name. | All of it, alone. Three releases of the bridge in twelve days. |
| Jun 2026 | CPU inference research: a 26-billion-parameter mixture-of-experts model running at 124 tokens per second on a desktop with no graphics card. Six days, a lab notebook, and a public write-up that includes the approaches that failed. | The measurements, the diagnosis, the quantization recipe, the write-up. |
| Jul 2026 | Teaching: a workshop where an 8-billion-parameter model went from 30 percent to 95 percent on a robot task, with a prompt written by an optimizer from the model's own failures. A model thirty times bigger scored 45 percent with the hand-written prompt. | The experiment, the deck, the notebook, and the feedback service behind it, shipped the same afternoon. |
| Aug to Sep 2026 | Three products on one platform: a webinar funnel product, an ad-acquisition product measured on a single number, and the transactional email service the other two send through. Each one runs and passes its full test suite with no vendor credentials at all. | All of it, alone. 127 unit tests and 179 endpoint tests on the webinar product, all passing before any vendor account existed. |
The retail platform was a team I led. Everything else in that table was one person. I can do both, and I know which one a given problem needs.
Before that
The twenty years before this were enterprise architecture: the systems that run a business rather than the ones that demo well. Seven of the companies I built along the way were sold. Each was built the same way, with a lean team, end-to-end ownership, and a refusal to outsource anything critical. I was the one writing the trading engine, deploying the infrastructure, acquiring the customers, and debugging production at three in the morning. That is still how I work. Production trading systems, client platforms and security infrastructure are kept in private repositories; the public ones are listed below.
How I work
Five habits, all of them from the records of real engagements rather than from a methodology book.
- The first thing I write is a decisions file. It lists the owner's decisions that I am not allowed to reopen, and the owner's rules for changing anything in production. Having it in writing stops me from arguing with those decisions later.
- Something ships every week that a real person can use. A page a real person can open, an email that sends, a report the owner can read. Work that nobody can see or use does not count as progress.
- Quality is enforced by the compiler. Code that can crash or hide a bug is rejected when the code is built. Tests, lints and type checks run before every merge. That is what makes it safe to move fast when AI agents are writing most of the code.
- Every external service is reached through an interface with two versions, a real one and a mock, chosen by whether the credential exists. The whole system runs and passes its tests before anyone gives me a key, so waiting on the customer's approvals never holds up the work.
- The handover is a written agreement. Who it is from and to, what shipped, what was deliberately not built and why, and a numbered activation order that names who does each step. The customer's team owns it fully afterwards.
The how it runs page has each of these with the figures and the failures.
On AI and this site, since it is a fair question for a site about AI: I use AI coding agents to build, with quality rules enforced by the compiler and the tests. The pages here are written by me, with AI used for research, checking and editing, and every number on them was measured on real hardware or counted from an engagement's records. Nothing is generated from memory.
What I am not
I am not an agency. You get me, not whoever is free. I do not sell a product or a platform, so my recommendation is not shaped by what I need to sell you. I do not write strategy decks that nobody implements; if the answer is a document, I will say so and not charge you for an engagement. And I am not a specialist in one industry. The last year covered retail, finance, security, video, robotics and inference research, and the method was the same in each: learn the rules of the business, build with its real data, ship into the systems it already uses, hand over.
Published work
- What is a forward deployed engineer?The role explained by someone who does it.
- Forward deployed engineering: how an engagement works, week by weekWeek by week, from the records of one engagement.
- What a forward deployed engineer does inside your companyWritten for business owners: the first month, the decisions, the handover.
- AI in retail: what gets built inside a retail chainNineteen months building a retail chain's operating platform.
- Fractional CTO: what it is, what it costs, and when to hire oneWhat the role does and costs, its risks, and how to tell whether you need a CTO, a Chief AI Officer, or neither.
- Fractional Chief AI Officer: what it is, what it costs, and when to hire oneWhat the role owns, the first thirty days, what it costs, and the cases where hiring one is a mistake.
- Forward deployed engineer vs solutions engineer vs sales engineer vs solutions architectFour roles side by side, and which one you need.
- Running Gemma-4 26B at 124 tokens/sec on a CPU, no GPUThe byte budget, the quantization recipe, the roofline, and the approaches that failed. Scripts in the public repository.
- Self-hosted LLM: how to keep your company's data privateThe four ways to keep data inside the company, sized by workload, with the running costs nobody budgets for.
- lerobot-genesis on PyPIThe bridge between the Genesis physics simulator and Hugging Face LeRobot. There was no official link between them; this package is that link. Open source, MIT.
- Tiny Model, Big JobsA workshop in two files: an 8B model from 30 percent to 95 percent with a prompt written by GEPA from its own failures. The deck is on this site; the notebook and source are in the public repository.
Common questions
Who is Arun Prasath E G?
An agentic AI architect and forward deployed engineer with twenty years in enterprise architecture and seven companies built and sold. I build production AI systems inside companies, with their own data and under their rules, and hand them over when they run. I also work as a fractional Chief AI Officer. The measured work is published on this site.
What does an agentic AI architect do?
Designs systems where AI models take actions rather than just answer questions: read the company's data, call its tools, follow its rules, hand results to the people and systems that need them. The architect decides what the model may read, what it must never do, who checks its work, and how it reaches the systems staff already use. Then builds it and makes sure it runs.
How is this different from hiring an AI agency?
An agency sells a team and a process, and you get whoever is assigned. I take on one or two engagements at a time and do the work myself, inside your company. I am not selling a product, so the recommendation is not shaped by what I have to sell. When it runs, I hand over and leave.
Which industries do you work in?
I do not specialize in one. In the last year the work covered a retail chain's operating platform, quantitative trading, on-chain monitoring, security research, a video product, warehouse robotics and CPU inference. The method is the same in each.
Do you work remotely?
Yes. Every engagement on this site was run remotely, inside the company's own accounts, repositories and systems, with a written weekly cadence. A decisions file and a weekly channel matter more than sitting in the same room.
How do I get in touch?
The form below asks three questions about the problem, or email parun@apeg.dev. I read every message myself and reply within two working days. If it is a fit I suggest times to talk. If it is not, I say so and why.
Arun Prasath E G
Agentic AI architect. Twenty years in enterprise architecture, seven companies built and sold, more than 8,000 commits in the last twelve months across a retail platform, an ad-acquisition platform, a transactional email service, a video product, robotics and CPU inference research. Every number on this site was measured on real hardware or taken from a real engagement's records.
Working with me
I take on one or two engagements at a time, inside the company, and I hand over when it runs. If you have a problem in your own processes that no single product solves, and you need it built and running, not just a recommendation, that is the kind of work I do.
Start here
What is a forward deployed engineer?The role explained by someone who does it: where the name came from, a normal week, the handover, and when you should not hire one.
The proof
Running Gemma-4 26B at 124 tokens/sec on a CPU, no GPUA measurement no competitor page publishes: the byte budget, the roofline, and the approaches that failed.