Senior MLOps Engineer

Satori Analytics

. Design and maintain infrastructure that takes ML models from experimentation to reliable, scalable deployment.

Posted 9/18/2026full-timeAthens • GreeceSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in MLOps and DevOps practices, focusing on the deployment and maintenance of machine learning models in production environments. Proficient in utilizing tools for model lifecycle management, observability, and infrastructure optimization across various cloud platforms.

Highest-signal resume keywords

MLOpsPython ProgrammingCI/CD PracticesDocker and KubernetesModel Deployment Tools

ATS Keywords

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Hard Skills

MLOpsPython ProgrammingModel Lifecycle ToolsCI/CD PracticesDockerKubernetesMonitoring ToolsInfrastructure-as-CodeFeature StoresModel Optimization Techniques

Soft Skills

Collaboration Skills

Tools & Technologies

MLflowKubeflowSageMakerVertex AIAzure MLBentoMLTorchServeTriton Inference ServerPrometheusGrafana

Industry Keywords

Machine LearningData GovernanceOperational ResilienceModel PerformanceData Drift

Tech Stack

Tools & technologies

AWSAzureCloudDockerGoogle Cloud PlatformGrafanaKafkaKubernetesPrometheusPythonTerraform

About the role

Key responsibilities & impact

Design and maintain infrastructure that takes ML models from experimentation to reliable, scalable deployment.
Create repeatable workflows for training, evaluation, deployment, and retraining, with versioning and reproducibility.
Package and deploy models as production services across cloud, on-premise, or hybrid environments.
Monitor model performance, data drift, system health, and production signals.
Collaborate with Data Scientists, AI Engineers, and Software Engineers to improve model testing, deployment, and maintenance.
Contribute to best practices for CI/CD, model registry, observability, security, and governance.
Deploy and optimize LLM-based systems, including inference services, GPU usage, and RAG infrastructure.
Ensure ML deployments follow strong access control, data governance, security, and operational resilience practices.

Requirements

What you’ll need

BSc or MSc in Computer Science, Software Engineering, or a related STEM field.
5+ years of experience in MLOps, DevOps, platform engineering, or ML engineering, with exposure to ML systems in production.
Strong Python skills and good software engineering fundamentals.
Hands-on experience with ML lifecycle tools such as MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML, or similar.
Experience deploying models using tools like BentoML, TorchServe, Triton Inference Server, or equivalent serving frameworks.
Strong experience with Docker, Kubernetes, CI/CD, and production-grade deployment workflows.
Comfortable working across cloud environments — AWS, Azure, GCP, or hybrid setups.
Experience with monitoring and observability tools such as Prometheus, Grafana, or similar.
Understanding of model performance, drift, retraining, reproducibility, and production reliability.
Strong collaboration skills — able to work across Data Science, Engineering, and client-facing teams.
Experience with Terraform, Pulumi, or infrastructure-as-code practices.
Experience with feature stores such as Feast or Tecton.
Familiarity with data and model versioning tools such as DVC or Delta Lake.
Experience with Kafka or event-driven ML workflows.
Hands-on experience serving LLMs in production using vLLM, TGI, Triton, or similar.
Familiarity with model optimization techniques such as quantization or GPU memory tuning.
Experience operating RAG infrastructure, vector databases, and embedding pipelines.
Exposure to LLM evaluation and observability tools such as LangSmith, RAGAS, or custom evaluation frameworks.

Benefits

Comp & perks

Competitive salary – A rewarding package that reflects your skills and experience.
Flexible Working – Enjoy flexible hybrid model in our modern Athens office or work remotely from anywhere in European economic Area (EU, Switzerland etc.) or UK (up to 6 weeks per year).
Health & Wellness Benefits – Private insurance, and the chance to work with a stellar crew.
Learning & Development – Training budget to level up your skills from the top tech partners in the market (Microsoft, AWS, Salesforce, Databricks etc.) – whether it’s certifications or courses, we’ve got you covered.
Career Growth – Clear opportunities to develop your skills and progress within the company.
Great Team & Culture – Work with talented, supportive people in a collaborative environment.
Team Events & Perks – Enjoy social events, celebrations, and exclusive employee benefits.
Modern Tools & Technology – Everything you need to do your best work.
A Workplace That Values You – Your ideas, contribution, and well-being genuinely matter.