Hello, I'm Malik.
Ten years of software engineering, 210+ projects at 5.0 on Fiverr, the last few years shipping LLM systems into real business software. Karachi, UTC+5.
Read about me →
Karachi, Pakistan · UTC+5
Open to new projects · replies within 24 hours
I build AI agents, LLM apps, and the API integrations that make them work in production.
10 years of software engineering; the last few shipping LLM systems into real business software. Not a chatbot that answers and stops.
The 24-hour read
Pick a problem. This is the shape of the read I'd send back within 24 hours.
RE: Invoices & documents
Pre-written · the real one follows a call
RE: Invoices & documents
- Approach
- Read the invoice, check it against your own systems, do the work: a bill in QuickBooks, a ticket in the helpdesk, a line in Slack.
- What breaks
- 429s, 5xx, timeouts — so retry with exponential backoff, duplicate and reconciliation checks, an approval threshold, and a replayable event log.
- Not AI
- I tell you when AI is the wrong tool. Sometimes a 40-line script beats an LLM: cheaper, faster, deterministic.
RE: CRM ↔ accounting sync
- Approach
- Two systems never designed to talk: webhooks, OAuth flows, rate-limited endpoints. Refunds, partial payments, and reconciliation handled automatically.
- What breaks
- Rate-limited endpoints and OAuth flows. Error handling, logging, and monitoring from day one, not after something breaks.
- Not AI
- Sometimes a 40-line script beats an LLM: cheaper, faster, deterministic. I say so even when the AI version would bill more hours.
- Proof
- Stripe ↔ QuickBooks sync for a logistics company. Has reconciled cleanly since launch without manual fixes.
RE: An assistant on your own data
- Approach
- Trained on your own data — Slack, Notion, Google Workspace, or your database — and answering with sources, so output is verifiable instead of confidently wrong.
- What breaks
- Confidently wrong answers. Sources on every answer make them verifiable; error handling, logging, and monitoring from day one, not after something breaks.
- Not AI
- I tell you when AI is the wrong tool. I say so even when the AI version would bill more hours.
- Tools
- Anthropic API · OpenAI API · open models · LangChain · LlamaIndex · RAG · Pinecone · Supabase pgvector · Chroma · evals
RE: An undocumented or legacy API
- Approach
- Third-party, undocumented, and legacy SOAP APIs: webhooks, OAuth flows, rate-limited endpoints. Half my career. Built to survive production, not just a demo.
- What breaks
- Rate-limited endpoints, OAuth flows, SOAP. Error handling, logging, and monitoring from day one, not after something breaks.
- Not AI
- Sometimes a 40-line script beats an LLM: cheaper, faster, deterministic. Either way, code you can hand to another developer: documented, structured, yours.
- Tools
- REST · GraphQL · webhooks · OAuth2 · SOAP · Python (FastAPI, Django) · Node.js (Express, NestJS) · PHP/Laravel · .NET/C#
- Timeline & cost
- Realistic estimates. Fixed scope, clear milestones, no surprise invoices.
AboutMalik Akhtar
- Based
- Karachi, Pakistan · UTC+5
- Overlap
- US mornings · EU afternoons
- Experience
- 10 years of software engineering
- Track record
- 210+ projects · 5.0 on Fiverr
- Replies
- Within 24 hours
- Language
- English
Hello, I'm Malik.
The engineer you hand the hard part to.
I've spent ten years building software for people who need it to work on Monday morning, not just in the demo. Two hundred and ten-plus projects with a 5.0 average rating on Fiverr, and enough production incidents to know what actually matters: honest scoping, boring reliability, and code you still own when I'm gone.
For the last few years that has meant putting LLMs to work inside real business systems — agents that read documents and take actions, assistants that answer with sources, integrations between tools that were never meant to talk. I like the unglamorous parts: retries, rate limits, logging, the monitoring that keeps a system running while you sleep.
I work from Karachi with clients in the US, UK, EU, Australia and the Middle East, overlapping US mornings and EU afternoons. You'll get written updates without asking, a realistic estimate, and a straight answer — including "you don't need AI for this" when that's the truth.
- 01
Honest scoping
A short scoping call before any estimate. I would rather tell you the project is simpler than you thought than take a bloated budget.
- 02
Boring reliability
Error handling, logging, and monitoring from day one, not after something breaks. Realistic estimates, fixed scope, clear milestones, no surprise invoices.
- 03
Code you still own
Documented, structured, yours. No lock-in, no black box, and support after delivery — I don't disappear at handover.
Work3 projects
Work
One you can open. Two I can only describe: client work stays private, so I show the shape of it instead.
01 AI operations agent live
Relay
Paper in. Actions out.
An AI operations agent that reads supplier invoices and customer emails, checks them against the company's own systems, and does the work: bills in QuickBooks, tickets in the helpdesk, a line in Slack.
- Role
- Design, backend and frontend. Solo project.
- Stack
- Python · FastAPI · LLM tool use · SSE · vanilla JS · pytest
- Guardrails
-
- Six tools with strict JSON schemas
- Duplicate and reconciliation checks
- Approval threshold
- Retry with exponential backoff for 429 / 5xx / timeouts
- Event log streamed over SSE — replayable, testable without a network
02 Client work · Logistics company
Stripe ↔ QuickBooks sync
Refunds, partial payments, and reconciliation handled automatically. Has reconciled cleanly since launch without manual fixes.
No public link — client work
03 Personal tooling
AI job-matching pipeline
Fetches listings, scores them against a profile, drafts proposals with an LLM, schedules follow-ups. FastAPI + SQLite + local dashboard, tested end to end.
No public link — personal tooling
What I build8 services
What I build
Systems that do the work. Not a chatbot that answers and stops.
- 01
AI agents & automation
Systems that do the work: process documents, qualify leads, research, draft, classify, and take real actions inside your tools.
- 02
AI inside your existing stack
LLMs wired into your CRM, database, admin panel, Google Workspace, Slack, Notion, HubSpot, Shopify, or a legacy system nobody wants to touch.
- 03
Complex API integration Half my career
Third-party, undocumented, and legacy SOAP APIs. Webhooks, OAuth flows, rate-limited endpoints, data syncs between systems never designed to talk.
- 04
RAG chatbots & assistants
Trained on your own data, answering with sources so output is verifiable instead of confidently wrong.
- 05
Backend & workflow systems
The APIs, queues, schedulers, and pipelines underneath all of the above. Built to survive production, not just a demo.
- 06
Email infrastructure & deliverability
Multi-domain sending, SPF/DKIM/DMARC, custom tracking domains, warmup, API-level integration.
- 07
Web apps & custom software
Full products and internal tools: React and Next.js front ends on Python or Node back ends, admin panels, dashboards, and the APIs underneath. Ten years of shipping software that is documented, structured, and yours.
- 08
Mobile apps
React Native and Flutter apps that share the API, auth, and data of your web product: iOS and Android from one codebase, with the backend built to match.
On paper8 points
What 10 years buys you
- I tell you when AI is the wrong tool. Sometimes a 40-line script beats an LLM: cheaper, faster, deterministic. I say so even when the AI version would bill more hours.
- Error handling, logging, and monitoring from day one, not after something breaks.
- Realistic estimates. I have been wrong enough times to know how long things take.
- Code you can hand to another developer: documented, structured, yours. No lock-in, no black box.
How I work
- Short scoping call first. I would rather tell you the project is simpler than you thought than take a bloated budget.
- Fixed scope, clear milestones, no surprise invoices.
- Regular written updates. You never have to ask what the status is.
- Support after delivery. I don't disappear at handover.
Stack6 groups
AI agents · Web apps · Mobile apps · API integration · RAG · LLM tool use · FastAPI · Node.js · Next.js · PostgreSQL · Redis · Docker · AWS · Stripe · HubSpot · Shopify · webhooks · OAuth2 · SOAP · MCP servers · evals · email deliverability
Stack
The tools I reach for, and the older ones I still add AI to.
- AI / LLM
- Anthropic API · OpenAI API · open models · LangChain · LlamaIndex · RAG · vector databases (Pinecone, Supabase pgvector, Chroma) · function calling / tool use · MCP servers · prompt engineering · evals
- Backend
- Python (FastAPI, Django) · Node.js (Express, NestJS)
- Frontend & mobile
- React · Next.js · TypeScript · React Native · Flutter
- Data & infra
- PostgreSQL · MySQL · MongoDB · Redis · Docker · AWS · CI/CD
- Integration
- REST · GraphQL · webhooks · OAuth2 · SOAP · Stripe · Twilio · HubSpot · Shopify · n8n · Make · Zapier
- Also
- PHP/Laravel · .NET/C# — for adding AI to systems that already exist
ContactReplies within 24 hours
Within 24 hours: an honest read on approach, timeline, cost.
Send me the problem. Short scoping call first — I would rather tell you the project is simpler than you thought than take a bloated budget.