╔═══════════════════════════════════════════════════════════╗
║ ASGHAR QAMBER RIZVI · AI/ML ENGINEER ║
║ Building systems that think, remember, speak ║
╚═══════════════════════════════════════════════════════════╝
Python AI engineer who builds end-to-end intelligent systems — from fine-tuning LLMs and designing RAG pipelines to deploying production FastAPI backends that handle real users. I care about things that actually work: low latency, correct retrieval, reliable APIs, and models that behave in production, not just in notebooks.
Currently finishing CS @ Bahria University (3.81 GPA, graduating June 2026). Based in Karachi, Pakistan. Available for remote roles.
Python · FastAPI · Gemini AI · PostgreSQL/PostGIS · JWT · Render · Supabase
An agentic platform for Pakistan's informal economy. Users describe a service need in natural language (Urdu, Roman Urdu, or English) — JUVO's multi-agent pipeline extracts intent, finds geographically nearby providers via PostGIS spatial queries, and creates a confirmed booking, all within a single conversational flow.
- Designed a multi-agent architecture: Intent Agent → Discovery Agent → Booking Service
- Built Hold-to-Lock (HTL) reservation system with 5-minute expiry and ACID database triggers to eliminate 100% of double-booking conflicts
- Implemented role-based JWT auth (user + provider), rate limiting, background task cleanup, and full Swagger documentation
- Deployed on Render + Supabase with Docker; full provider dashboard with analytics
Python · LangChain · Llama-3 (3B) · LoRA · MongoDB Vector Atlas · ChromaDB · RAG
Domain-specific legal AI assistant built on fine-tuned Llama-3 with a production-grade RAG pipeline.
- Fine-tuned Llama-3 (3B) using LoRA (r=32, α=64) on criminal law cases and legal statutes for improved legal reasoning
- Engineered a vector database on MongoDB Vector Atlas — 0.98 accuracy on past cases and legal statutes
- Production-level techniques: streaming responses, caching, rate limiting — query speed reduced to 200ms
- Chat and message management via LangChain memory components
Python · FastAPI · WebSocket · Computer Vision · Pose Estimation
Real-time fitness AI built during internship at Meta Frolic Labs.
- Custom computer vision pipeline with pose estimation algorithms — 98% accuracy in live exercise detection
- FastAPI backend with WebSocket integration for real-time data streaming at 0.3ms latency
- Performance analysis algorithm evaluating form quality and delivering corrective suggestions through a web interface — 40% improvement in feedback system effectiveness
Python · SpeechT5 · Transformer · Multi-speaker TTS
Speech synthesis system targeting the voice of Zia Mohyeddin, Pakistan's most celebrated literary narrator.
- Developed SpeechT5 transformer-based architecture with multi-speaker capabilities
- 25% improvement in naturalness over existing Urdu TTS solutions
- 92% user preference in blind listening tests for voice fidelity
Python · PyTorch · GANs · Transformers · Audio Processing
Research project: converting emotional tone in speech while preserving semantic content.
- Designed a hybrid GAN + Transformer architecture — Transformer vector embeddings carry semantic context; GAN learns the target emotion tone
- Achieved 62% success rate on neutral → happy emotion conversion
- Custom approach combining contextual sentence embeddings with adversarial training for audio emotion transfer
Python · Transformers · Fine-tuning · NLP
Fine-tuned a language model specifically for high-quality paraphrase generation — preserving meaning while altering structure and vocabulary. Trained and evaluated on custom paraphrase datasets.
LLM & AI Llama-3 · SpeechT5 · LoRA fine-tuning · GANs · Transformers
Prompt engineering · RAG pipelines · LangChain · ChromaDB
MongoDB Vector Atlas · Vector search · Embedding models
Computer Pose estimation · Real-time inference · WebSocket streaming
Vision OpenCV · MediaPipe
Backend FastAPI · Django · Flask · SQLAlchemy · Pydantic
JWT auth · Rate limiting · Background tasks · REST APIs
Databases PostgreSQL · PostGIS · MongoDB · Vector databases · Redis
MLOps & Docker · Render · Supabase · Alembic · Gunicorn · Nginx
Deploy Multi-user concurrency · Production API design
Languages Python (primary) · SQL · C++ · Java
| Metric | Value |
|---|---|
| Exercise detection accuracy | 98% |
| API latency (WebSocket, real-time CV) | 0.3ms |
| Legal RAG retrieval accuracy | 0.98 |
| Query speed (RAG + streaming) | 200ms |
| Urdu TTS improvement over baseline | +25% |
| Voice clone user preference (blind test) | 92% |
| Emotion conversion success rate | 62% |
| Feedback system effectiveness improvement | +40% |
| GPA | 3.81 / 4.0 |
Python AI Trainee — Meta Frolic Labs (Aug 2025 – Oct 2025) Shipped real-world AI systems under senior engineers: real-time computer vision pipeline, emotion conversion research (GAN + Transformer), and Urdu TTS with voice cloning. Production-level work with FastAPI, WebSocket, and transformer fine-tuning.

