About the job
About Aizen
At Aizen, our mission is to simplify AI and amplify its capabilities. We offer a comprehensive platform that optimizes the entire AI workflow—from data ingestion and orchestration to model training, deployment, and monitoring—empowering businesses to focus on what truly matters: innovating and scaling exceptional AI solutions without the complexities.
Frustrated by cumbersome AI processes and fragmented solutions, we founded Aizen to revolutionize AI adoption. Today, businesses of all sizes—from budding startups to Fortune 500 giants—rely on us to seamlessly manage their AI pipelines and scale with ease. By redefining the development, deployment, and management of AI, we're making it more accessible and impactful than ever. If you have a passion for technology, enjoy solving real-world challenges, and want to collaborate with a top-notch engineering team, we would be thrilled to connect with you.
About the Team
Founded by a group of visionary entrepreneurs with extensive backgrounds in data storage, distributed systems, and real-time AI architecture, Aizen's team has a proven track record in building and scaling successful tech companies, with some achieving $1B+ exits. Our collective expertise has shaped Aizen into a platform designed to simplify the AI pipeline, enhance performance, and make AI truly accessible for enterprises of all sizes.
About the Role
As a Machine Learning Engineer at Aizen, you will play a crucial role in building and refining the AI pipelines that power our end-to-end platform. You will engage with the complete stack—from data ingestion and model training to real-time inference and monitoring—ensuring efficient AI deployment at scale. Whether you are an entry-level engineer eager to learn or a seasoned professional ready to spearhead impactful projects, this position presents an exciting opportunity to tackle complex ML challenges, enhance automation, and influence the future of AI infrastructure.
Core Responsibilities
AI Pipeline Development – Design, implement, and optimize comprehensive AI pipelines for data ingestion, training, deployment, and real-time inference, ensuring smooth integration with MLOps and infrastructure systems.
Model Training & Deployment – Execute training and fine-tuning workflows for ML models, maximizing efficiency, scalability, and reproducibility across cloud, on-premises, and edge environments.
Backend & API Development – Create and integrate scalable backend services and APIs for model inference...

