AI Product & Operations Lead

I take AI products from idea to production, and cut what they cost to run.

I own the whole path, from research and system design to hiring, testing and launch. At AlphaRexX that meant a deterministic strategy engine that helped cut testing cost by 97%, and an LLM pipeline reduced from 370K tokens per run to 30K. I work comfortably with engineers and with founders who don't write code, and I report results in time and money saved.

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  • 3+ years working with AI tools
  • 4+ years in financial markets
  • Managed up to 6 contributors
97%Lower strategy testing costContributed through a deterministic DSL engine
92%Fewer LLM tokens per run370K down to 30K, and API calls from 6 to 2
30%Lower monthly costAI-assisted workflows replaced an outsourced developer, and turnaround fell from 30 to 15 days
30daysTo commercial launchGetIntakes, from initial planning to live
Available now, full time, remote

Ready for one full-time role.

AlphaRexX is the platform I founded, built and brought to an automated, production-ready stage. The architecture is stable and handed over to my core engineering team for maintenance. I am closing this operational chapter to bring my cost reduction skills to your company full time.

Contact me View Resume
Experience

AlphaRexX

An AI-powered SaaS platform that turns natural-language trading strategies into structured, backtestable execution bots. It is live at alpharexx.io.

Founder & Product Lead AlphaRexX, March 2025 to September 2026

Built it from the ground up, managing product strategy, hiring, budgets and delivery. Defined the architecture, system workflows and UI/UX direction, and led execution to live production deployment.

40%Less server memoryLower memory usage on the same workload.Reduced
60%Faster strategy reportsReport generation time cut for every backtested strategy.Reduced
2 sVoice agent response timeAverage, on Google Gemini Multimodal APIs.Led development and testing
1,000+Strategies testedPlatform testing coordinated across the full set.Coordinated
93.55%Rule-matching accuracyNatural-language strategy parser, across the strategies tested.Oversaw testing
89%Filtered as non-viableStrategies that failed the platform's filtering during testing.Platform filtering
The pipeline

From a video to a backtest.

A trader finds a strategy explained in a video. The platform turns it into exact rules and tests it against past market data. This is how one run moves through the system.

In plain terms

The strategy goes in as words. It comes out as precise rules that a computer can test the same way every time, and the user chooses which version to run.

Under the hood

Captions are fetched through a residential IP, because YouTube blocks data center traffic. Gemini extracts entry, exit and risk logic. The DSL engine builds the rules deterministically, which replaced LLM-based strategy code generation and contributed to the 97% drop in testing cost. Written strategies skip the caption step.

INTAKE LANGUAGE MODEL DSL ENGINE AND BACKTEST 1 Strategy submitted A YouTube link or a written description video link 2 Captions fetched Video links only, through a residential IP caption text 3 Strategy extracted Gemini returns entry, exit and risk logic structured strategy 4 Rules built DSL engine, same input gives the same rules executable rules 5 Candidates shown Two to three strategies to choose from the user's pick 6 Full backtest Run on the strategy the user picks
One run from submission to backtest. The language model extracts the strategy, and everything after that runs on the rules the DSL engine builds.
How I work

Research first. Failure cases second. Code last.

Every feature goes through the same sequence before anyone writes code. Most of the cost is decided in the first three steps.

  1. 1

    Research

    How the thing can be built and what it needs: data, APIs, infrastructure.

  2. 2

    Find where it fails

    Drawbacks, downsides, leaks and failure cases, listed before they happen in production.

  3. 3

    Check the cost

    Where cost appears, how to manage it, and how to optimize the design before building.

  4. 4

    Write the plan and the acceptance criteria

    Each part is defined, including what done means, so a developer or an AI tool can build it without guessing.

  5. 5

    Build and test, failure cases first

    The builder focuses on the failure cases, tests them and fixes them immediately. After the build I test again and give the failure cases the most weight.

How I work with code. I design, specify and review. Specialists and AI tools write the code, and I verify the result against the acceptance criteria.

Team and delivery

I hired, trained and ran a remote team of up to six.

At AlphaRexX I recruited and managed engineers, interns and freelancers, and made the final call on every feature added or removed.

Hiring

Recruited, onboarded and managed contributors across engineering and AI, within budget. I also replaced an outsourced developer with AI-assisted workflows, which cut monthly costs by 30%.

AI adoption

Trained the team on AI tools and workflows, which raised overall delivery throughput by about 2x.

Delivery

Ran daily standups, prioritized tasks, and oversaw pre-deployment testing on staging before anything shipped.

How a change ships

  1. STEP 1DefineThe feature and the research behind it.
  2. STEP 2SpecifyThe plan and the testing criteria go to the builder.
  3. STEP 3Pull requestThe builder opens one when the work is ready.
  4. STEP 4Review and mergeI review and test it with AI-assisted checks, then merge.
Selected project

GetIntakes

An automated healthcare intake platform, taken from initial planning to commercial launch in 30 days. getintakes.com

AI Product & Workflow Consultant Contract, September 2026 to October 2026
  • Planned and delivered the core workflows of the platform.
  • Mapped clinical intake journeys and designed rule-based workflows for lead triage, surgical contraindication checks and secure Stripe deposits.
  • Designed AI response workflows using the practice's SOPs, which cut repetitive coordinator work and sped up lead-to-booking qualification.
Background

A founder who also knows the market.

I understand the trading side of what I build, which is why AlphaRexX is organized around strategies, backtests and risk rules.

Financial markets

  • 4+ years in financial markets, across trading, spot and derivatives, and risk management.
  • Used results from 1,000+ tested strategies to tighten the platform's rules and filtering.

Founder

  • Took a product from zero to live production.
  • Owned strategy, budgets, hiring and delivery.
  • Launched a client's healthcare intake product in 30 days.
Contact

Open to full-time remote roles worldwide.

Email faraz@alpharexx.io Send Email →
Resume (PDF) View Resume Online → Download Resume (PDF) ↓
LinkedInmuhammad-faraz-khan
Productalpharexx.io
LanguagesUrdu (native). English (upper intermediate, strong written communication).