Ph.D. in High-Performance Computing, with deep expertise in data center networking and AI infrastructure.
About
Built by engineers who've run AI infrastructure at scale.
ParallelIQ helps teams bring discipline, visibility, and efficiency to production AI.
Operators ship the product.
Every recommendation we surface is reviewable, reversible, and auditable. Humans stay in the loop, by design.
Inference is not web traffic.
GPU workloads have memory shape, KV cache, batch dynamics. Generic schedulers were never built for this.
Visibility before automation.
Before we touch your fleet, we explain it. No black boxes — every decision shows its work.
Meet the team
Built by engineers who've run AI infrastructure at scale.
Bridging AI strategy with real-world DevOps, MLOps, and AIOps execution.
AI infrastructure and cloud security.
Yurii Maslianchuk
Senior Engineer, MLOps
Kubernetes/MLOps engineer for infrastructure — GPU and model serving.
Why we built ParallelIQ
GPU inference waste is invisible until it shows up on your bill. We built ParallelIQ because we've been on the other side of that problem — managing production AI infrastructure without the tools to see what's actually happening or fix it with confidence.