Viktor Yarmolenko
I specialise in bridging the gap between advanced research and production-grade engineering. With a PhD in Physics and an MSc in Machine Learning, and over a decade engineering experience, I bring a scientist’s precision to high-performance cloud architecture.
My focus is on Systemic Efficiency: I recently delivered a 4x reduction in ML pipeline latency (from 16h to 4h) and a 35% optimisation in cloud infrastructure spend for a Tier 1 autonomous vehicle firm. I am driven by "Infrastructure as a Product", ensuring that complex research reaches scale with maximum reliability and minimum cost.
Core Expertise:
Orchestration: Leading 6-12 month technical initiatives across global sites (London, Tokyo, Seattle, Palo Alto).
Force Multiplication: Building observability frameworks that reclaim 20% of engineering bandwidth.
Deep Tech: Architecting low-latency distributed systems for AV, Big Data, and Physics-based simulations.
Tech Stack: Go, Java, Python, AWS, Kubernetes, Distributed Systems.
What I do
Machine learning in production
Design and delivery of ML-enabled systems: data pipelines, model lifecycle, observability, and reliability.
Software engineering at scale
Backend systems, distributed architectures, performance, and operational excellence.
Pragmatic problem solving
Clear trade-offs, incremental delivery, and robust implementations with a bias for maintainability.
Highlights
- Current focus: ML platform engineering, developer productivity, applied AI.
- Core stack: Go, Java, Python, AWS, Kubernetes, Distributed Systems.
- Location: United Kingdom (remote / hybrid).
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