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Senior MLOps Engineer
Satori AnalyticsSenior MLOps Engineer building and deploying AI infrastructure at Satori Analytics. Transforming ML models into reliable, scalable production-ready solutions in the Athens office.
Tech Stack
Tools & technologiesAWSAzureCloudDockerGoogle Cloud PlatformGrafanaKafkaKubernetesPrometheusPythonTerraform
About the role
Key responsibilities & impact- **What Your Day Might Look Like:**
- - **Make AI production-ready:** Design and maintain the infrastructure that takes ML models from experimentation to reliable, scalable deployment.
- - **Build automated ML pipelines:** Create repeatable workflows for training, evaluation, deployment, and retraining — with versioning and reproducibility built in.
- - **Deploy and serve models:** Package models as production services across cloud, on-premise, or hybrid environments, with performance and reliability in mind.
- - **Monitor what matters:** Track model performance, data drift, system health, and production signals to support better retraining and troubleshooting decisions.
- - **Enable AI teams:** Work closely with Data Scientists, AI Engineers, and Software Engineers to improve how models are tested, deployed, and maintained.
- - **Set the standard:** Contribute to best practices around CI/CD, model registry, observability, security, and governance.
- - **Support GenAI at scale:** Help deploy and optimize LLM-based systems, including inference services, GPU usage, and RAG infrastructure where needed.
- - **Keep systems secure and reliable:** Ensure ML deployments follow strong practices around access control, data governance, and operational resilience.
Requirements
What you’ll need- **Your Superpowers🚀:**
- - 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.
- **Bonus Points for:**
- - 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- **Perks on Perks:**
- - Competitive salary and hybrid work model – come hang out in our Athens office or work remotely from anywhere in European economic Area (EU, Switzerland etc.) or UK (up to 6 weeks per year).
- - 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.
- - Private insurance, top-tier tech gear, and the chance to work with a stellar crew.
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
MLOpsDevOpsplatform engineeringML engineeringPythonMLflowKubeflowSageMakerDockerKubernetes
Soft Skills
collaborationcommunicationtroubleshootingorganizationalleadership
Certifications
BSc in Computer ScienceMSc in Computer ScienceBSc in Software EngineeringMSc in Software Engineering