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I will high performance fastapi backends with asyncpg and ml
Kaushik
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High-Performance FastAPI & ML Backend Architect
Stop settling for blocking APIs that crash under load. I build high-concurrency architectures designed for 2026 standards. Leveraging FastAPI and Asyncpg, I deliver non-blocking, production-grade backends that maximize throughput for data-intensive AI applications.
As a Senior Developer, I don't just write scripts; I architect scalable systems that bridge the gap between ML models and real-world production.
What I Deliver:
- Asynchronous Mastery: Non-blocking database operations with Asyncpg and PostgreSQL for maximum performance.
- Production-Grade Security: Robust authentication (JWT/Argon2) and strict data validation via Pydantic v2.
- Advanced AI Integration: Optimized inference for Deep Learning models with support for background retraining tasks.
- RAG & Vector Search: Expert implementation of hybrid search (Vector + BM25) and Qdrant integration for proprietary data.
- Self-Improving Systems: Specialized user-trust and feedback logic to track model performance in real-time.
Why Choose Me?
I specialize in hardware-aware inference (ONNX/CUDA) and memory management to ensure your app runs lean and fast on any infrastructure.
Conoce a Kaushik
Kaushik
Senior AI Engineer High Concurrency FastAPI Local PyQt5 ML
- DeIndia
- Miembro desdemar 2026
- Responde aprox. en:9 horas
Idiomas
Telugu, Inglés
Senior AI Engineer specializing in high-concurrency systems and local DL deployment. I bridge the gap between ML research and production software.
Expertise:
• FastAPI/Asyncpg: High-throughput, non-blocking backends with Pydantic v2.
• Local AI & PyQt5: Secure, standalone desktop apps running PyTorch/ONNX models.
• Advanced RAG: Hybrid search (Vector + BM25) with Cross-Encoder reranking.
• Efficiency: Hardware-aware inference (CUDA/ONNX) and optimized memory management.
I build proprietary, high-performance intelligence that scales beyond simple API wrappers. Let’s build.
FAQ
Why do you use Asyncpg instead of standard libraries like SQLAlchemy or Psycopg2?
Standard libraries often block the event loop, causing your API to slow down or crash under heavy traffic. I use Asyncpg because it is a native, non-blocking PostgreSQL driver that handles thousands of concurrent requests with significantly lower latency, ensuring your AI backend stable
Can you integrate local AI models to avoid high OpenAI/Claude API costs?
Yes. Unlike "wrapper" developers, I specialize in local deployment. I can integrate Deep Learning models directly into your FastAPI backend using ONNX Runtime for hardware-aware inference (CPU/CUDA). This ensures 100% data privacy and zero recurring per-token costs for your business.
Do you handle the database schema design and migrations?
For the Standard and Premium tiers, yes. I design optimized PostgreSQL schemas and set up the initial database architecture using asynchronous patterns. For the Basic tier, I provide the connection logic, but I expect a pre-existing schema or a very simple structure.
Can my API handle background tasks like model retraining or data scraping?
Absolutely. In your production environment, I implement Background Tasks so that long-running processes (like the retraining logic found in my user_trust_system.py) do not block the user's request. This keeps your application responsive while complex AI operations run in the "shadows."
