MLOps Engineer

Job description

About Alta Ares Alta Ares is an air defense Neoprime. We build AI-guided interceptors to counter drones and cruise missiles, along with the software platform that powers them. Our customers are NATO-aligned militaries and defense institutions, and our systems are regularly deployed during live exercises and operational demonstrations across Europe, the Middle East, and Asia. Founded in 2024, we have grown to 90 people, raised over $60M, and increased revenue 40× this year. We are now entering a new phase of international expansion, industrial scaling, fundraising, and product development. Role & Mission As an MLOps Engineer , you will own the evolution of Gamma Ulixes , the AI product platform powering both our internal development workflows and customer-facing AI capabilities. You will work alongside Machine Learning Engineers, Software Engineers and DevOps Engineers to transform research prototypes into production-ready AI systems. Beyond infrastructure, you will help shape the tooling, services and workflows that accelerate the entire machine learning lifecycle, from data ingestion to deployment on operational platforms. This role is ideal for engineers who enjoy building products, automating complex workflows and enabling AI systems to scale reliably. Your Responsibilities Gamma Ulixes AI Platform Develop and improve Gamma Ulixes, our AI platform for training, evaluating and deploying machine learning models. Build experiment tracking, model registry and dataset management capabilities. Design reproducible ML workflows and automated training pipelines. Develop benchmarking, evaluation and model validation services. ML Infrastructure Design scalable infrastructure supporting computer vision and trajectory-based machine learning. Build APIs and backend services powering AI workflows. Manage GPU infrastructure, distributed training and resource orchestration. Improve monitoring, observability and reliability across AI services. Maintain reproducible development and deployment environments. Deployment & Edge AI Build CI/CD pipelines for machine learning models. Deploy AI services across cloud, on-premise and edge environments. Optimize inference pipelines for Nvidia Jetson and embedded hardware. Manage model packaging, versioning and rollout strategies. Improve deployment reliability across operational environments. Collaboration Work closely with Machine L…