You will design and operate the systems behind the AI stack, from model training and evaluation to deployment, inference, observability, and continuous improvement. You will work closely with AI engineers, researchers, and product engineers to turn models into reliable, scalable, and cost-efficient production systems. You will build the platforms, tooling, and infrastructure that enable the team to experiment quickly and bring AI capabilities to production with confidence.

Design systems for model training, evaluation, deployment, inference, and experimentation Develop reliable pipelines for data preparation, training, evaluation, model release, and continuous improvement Build platforms and tooling that enable AI engineers and researchers to experiment, evaluate, and ship models faster Develop evaluation and benchmarking infrastructure to measure model quality, performance, and regressions Build production observability, monitoring, tracing,



and alerting for AI/ML workloads Identify bottlenecks across the ML stack and continuously improve system performance Work closely with AI engineers, researchers, and product teams to turn evolving model requirements into production-ready infrastructure Python PyTorch / JAX Cloud infrastructure ML/data pipelines and workflow orchestration GPU infrastructure and performance tooling Vector databases and retrieval infrastructure Strong software engineering fundamentals and experience building production systems Experience building ML infrastructure, platforms, or production machine learning systems Experience with model deployment, inference, evaluation, or data pipelines Ability to write clean, maintainable, production-quality code AI infrastructure reliably supports production workloads at scale

📌 Machine Learning Data Engineer (La Mancha)
🏢 ActAI
📍 La Mancha