MLOps Engineer

Job not on LinkedIn

🔥 1 hour ago

🇬🇷 Greece – Remote

💵 €70k - €100k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 0%

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KIEFER

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2014

🏗️ Construction

🏥 Healthcare

📦 Logistics

Construction • Healthcare • Logistics

KIEFER is an energy technology company focused on developing and delivering technologies that improve the production, storage, distribution, or efficiency of energy systems. The company works at the intersection of engineering and energy innovation to provide solutions for utilities, industrial operators, and other energy stakeholders aiming to reduce costs, increase reliability, and lower environmental impact.

📋 Description

• 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 • Work with teams to resolve recurring production issues • Collaborate closely with ML and Engineering teams to make experiments reproducible and model deployment and production operations reliable

🎯 Requirements

• Hands-on experience building or operating infrastructure for production ML systems • Strong Python and Linux skills • Ability to write maintainable production code • Practical understanding of the ML lifecycle, from experiments and evaluation to model deployment • Experience with containers, workload orchestration, CI/CD, and infrastructure automation • Experience with GPU workloads and measuring performance, utilization, and cost • Experience monitoring and troubleshooting production systems • 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

🏖️ Benefits

• Competitive package aligned with talent benchmarks • Hands-on work shaping AI capabilities for products and real-world applications • AI-native environment with challenges across model development, evaluation, and production AI systems • Engineering-first culture • High autonomy and low bureaucracy • Space to shape meaningful AI products