Nadia Volochii — Business Development Executive
Most AI companies hire backend developers who think ML is just another API, then wonder why models never reach production. The hiring disasters I prevent: → Backend devs building APIs that can't handle model inference latency → "Senior" engineers who've never dealt with GPU memory management → Infrastructure teams optimizing for web traffic, not ML workloads RECENT MATCHES: AI Startup: Backend team reduced model serving latency from 2.3s to 45ms, made product viable Computer Vision: Infrastructure specialists built pipeline serving 50M predictions daily NLP Platform: ML-aware developers deployed models that scale with user growth I find developers who understand ML infrastructure constraints, not just general backend development. When your AI needs developers who get ML production reality, let's talk.
Stackforce AI infers this person is a Backend Developer specializing in AI infrastructure and model deployment.
Experience: 2 yrs 9 mos
Skills
- Ai Development
- Client Solutions Management
- Infrastructure Development
Career Highlights
- Expert in connecting AI projects with production-ready data scientists.
- Specializes in ML infrastructure constraints for backend development.
- Proven track record in reducing model serving latency.
Work Experience
Digit-point
IT Consultant (2 yrs 8 mos)
Hygge Software
Client Solutions Manager (2 yrs 9 mos)
Education
3 at Uniwersytet WSB Merito Wrocław
