MLOps Engineer

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

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Develop and improve Gamma Ulixes, our AI platform for training, evaluating and deploying machine learning models.

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Build experiment tracking, model registry and dataset management capabilities.

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Design reproducible ML workflows and automated training pipelines.

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Develop benchmarking, evaluation and model validation services.

ML Infrastructure

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Design scalable infrastructure supporting computer vision and trajectory-based machine learning.

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Build APIs and backend services powering AI workflows.

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Manage GPU infrastructure, distributed training and resource orchestration.

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Improve monitoring, observability and reliability across AI services.

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Maintain reproducible development and deployment environments.

Deployment & Edge AI

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Build CI/CD pipelines for machine learning models.

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Deploy AI services across cloud, on-premise and edge environments.

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Optimize inference pipelines for Nvidia Jetson and embedded hardware.

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Manage model packaging, versioning and rollout strategies.

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Improve deployment reliability across operational environments.

Collaboration

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Work closely with Machine Learning Engineers to industrialize research workflows.

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Collaborate with Software Engineers to integrate AI capabilities into operational products.

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Help define engineering standards, tooling and best practices across the Data & AI team.

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Contribute to the evolution of Alta Ares' AI platform roadmap.

Requirements

Candidate Profile

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3–5+ years of experience as an MLOps Engineer, ML Platform Engineer, Software Engineer or Data Engineer working on production machine learning systems.

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Strong Python software engineering skills.

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Experience designing and operating production-grade ML infrastructure.

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Solid understanding of Docker, Linux and containerized environments.

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Experience with ML experiment tracking, model registries and reproducible workflows.

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Experience designing APIs and backend services supporting AI applications.

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Familiarity with CI/CD, infrastructure automation and cloud-native development.

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Strong ownership mindset with the ability to drive projects from design to production.

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Comfortable collaborating across Machine Learning, Software and Infrastructure teams.

Skills & Values

Product Ownership

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Build products, not just infrastructure.

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Focus on developer experience and operational excellence.

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Deliver solutions that scale across teams and customers.

Platform Engineering

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Design reliable, maintainable and observable AI systems.

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Automate repetitive workflows.

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Continuously improve engineering productivity.

Execution & Autonomy

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Comfortable taking ownership of complex technical projects.

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Pragmatic, impact-driven and hands-on.

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Thrive in fast-moving environments with high technical standards.

Collaboration

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Work effectively across AI, Software and Infrastructure teams.

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Communicate clearly and document engineering decisions.

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Contribute to a strong engineering culture built around quality and continuous improvement.

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