MLOps Engineer

KIEFER

. Build workflows for running and managing model training and fine-tuning jobs .

Posted 10/20/2026full-timeRemote • GreeceMid-LevelSenior💰 €70,000 - €100,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and managing production ML systems, with a strong focus on automation, monitoring, and performance optimization. Proficient in Python, Linux, and various ML tools to ensure reliable model deployment and operations.

Highest-signal resume keywords

Production ML Systems InfrastructurePython ProgrammingML Lifecycle UnderstandingGPU Workload OptimizationML Tooling Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonLinuxModel DeploymentWorkload OrchestrationCI/CDInfrastructure AutomationMonitoring Production SystemsDistributed TrainingMulti-GPU TrainingModel Artifact Versioning

Soft Skills

CollaborationProblem-Solving

Tools & Technologies

MLflowWeights & BiasesKubeflowRayVLLMSGLangNVIDIA Triton

Industry Keywords

GPU Workload SchedulingData GovernanceModel EvaluationModel Fine-TuningProduction Operations

Tech Stack

Tools & technologies

CloudLinuxPythonRay

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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