MLOps Engineer

Description

KIEFER is building Greece's integrated AI ecosystem. From renewable energy infrastructure and AI systems to robotics and enterprise applications, we connect the technologies that power Europe's intelligent future. Founded in 2014, KIEFER has delivered 600MW+ of energy projects and is now developing sovereign AI infrastructure, enterprise AI products and physical AI systems for Greece and Southeast Europe.

About the role

We are looking for an MLOps Engineer to build the infrastructure and tools that support ML work at Kiefer Tech. Our work spans Sophea AI language model, the Sophea Nous RAG platform, robotics, and Hyperion X, our energy trading platform.

You will work closely with ML and Engineering teams to make experiments reproducible and model deployment and production operations reliable.

Responsibilities

• Build workflows for running and managing model training and fine-tuning jobs.
• Make experiments traceable through dataset, checkpoint, and model artifact versioning.
• Automate model evaluation and release checks so teams can compare changes before deployment.
• Build deployment and serving workflows, including safe rollout and rollback of model versions.
• Improve GPU workload scheduling and utilization, with attention to performance and compute cost.
• Add monitoring and troubleshooting tools for production ML systems, and work with teams to resolve recurring issues.
• What you will need
• Hands-on experience building or operating infrastructure for production ML systems.
• Strong Python and Linux skills, with the ability to write maintainable production code.
• A practical understanding of the ML lifecycle, from experiments and evaluation to model deployment.
• Experience with containers, workload orchestration, CI/CD, and infrastructure automation.
• Experience working with GPU workloads and measuring performance, utilization, and cost.
• Experience monitoring and troubleshooting production systems.

Requirements

• Experience with LLM serving tools such as vLLM, SGLang, or NVIDIA Triton.
• Experience with distributed or multi-GPU training.
• Experience with ML tooling such as MLflow, Weights & Biases, Kubeflow, or Ray.
• Experience supporting ML workloads in private cloud or on-premises environments with data governance requirements.
• What is there for you
• Compensation: Competitive package aligned with talent benchmarks.
• Impact: Hands-on work shaping AI capabilities for Kiefer Tech’s products and real-world applications.
• AI-native environment: Real challenges across model development, evaluation, and production AI systems.
• Culture: Engineering-first, high autonomy, low bureaucracy, and space to shape meaningful AI products.