From deployment to scaling, find people who turn AI models into reliable, production‑ready systems.
Showcasing people who’ve launched real‑world AI solutions
London, UK | 10+ Years
Experience
$195/hr
Berlin, Germany | 9+ Years
$210/hr
Bangalore, India | 8+ Years
Category: MLOps & CI/CD
A large e-commerce company hired an MLOps Engineer to build an automated CI/CD pipeline for their recommendation engine, significantly accelerating release cycles and model updates.
Budget: $45,000
Duration: 4 months
Remote (Global)
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Category: RLHF Curation
A conversational AI startup engaged an RLHF Curator to collect and rank human preference data to refine chatbot tone and helpfulness.
Budget: $25,000
Duration: 2 months
Remote (UK)
Category: AI Infrastructure & Cloud Computing
A healthcare tech firm engaged an AI Infrastructure Engineer to design and implement a highly available, scalable infrastructure on GCP for their diagnostic AI models, ensuring rapid and reliable inferences.
Budget: $50,000
Duration: 5 months
Remote (USA)
An MLOps Engineer helps deploy, monitor, and manage machine learning models in production. They bridge the gap between data scientists and engineering teams—automating workflows, managing model versions, setting up CI/CD for ML, and ensuring the model runs smoothly at scale.
They build the backend systems that allow AI models to run reliably and scale up when needed. This includes:
Yes, these engineers set up automated pipelines that:
This speeds up deployment and ensures consistent performance.
RL is compute-heavy and interactive. Key things to consider:
It’s like building infrastructure for both AI and gaming at once.