Here is what you'll actually do.
You'll bridge the gap between business problems and AI implementation. For 1-2 concurrent projects, you will digest client requirements, design systems involving LLMs and vector databases, rapidly prototype the solution, build the production pipeline, and hand off an intelligent API or agent system. You ship working AI, not just Jupyter notebooks.
Recent task cards you would have pulled.
[ RAG Systems ]Complex conversational interfaces with semantic search
[ Agentic Workflows ]Multi-agent frameworks, LangChain pipelines, automated task execution
[ Python APIs ]FastAPI servers wrapping model calls with low latency
We are extremely specific about who fits.
You're right for this role if...
✓You've shipped something real and can show us the commit history
✓You understand that a reliable 80% solution beats a theoretical 99% accuracy model that never ships
✓You communicate progress before you're asked for an update
✓You follow the AI space hourly and know which new model invalidates our current approach
This role isn't right for you if...
✕You need detailed tickets with every edge case spelled out
✕You measure success by hours logged, not features shipped
✕You're looking for a stepping stone — we want people who want to stay
Tools of the trade.
Must Have:
PythonOpenAI API / ClaudeLangChain / LlamaIndexVector DBs (Pinecone/Weaviate)FastAPI
Nice to Have:
Hugging FaceAWS/GCP ml-ops deploymentsNext.js for quick UIs
We'll teach you:
Advanced prompt engineering for specific edge casesTypescript backend basics
Honest numbers. No fluff.
Base Salary
PKR 1M - 1.5M. Determined by your past shipped work.
Performance Track
USD Performance Track after 6 months. For top performers with high sprint velocity.
Absolute Flexibility
Async, outcome-based. No time tracking. Deliver features, not hours.
Public Growth
Case study credit. Your work becomes your portfolio. Your name goes on it.
How we decide.
01
Apply
~10 minForm below. No CV required.
02
Skill Task
48 hrsYou'll receive a dataset and a problem statement. Build a simple RAG pipeline using a vector DB and an LLM to answer questions accurately with citations. Return a working API endpoint. 48 hours.
03
30-min Call
~30 minWe discuss your code.
04
Offer
24 hrsYes or No, within 24 hours.
Build with us.
Submit your best work to start the process.