Dialectica

ML Ops Engineer

Dialectica

full-time

Posted on:

Location Type: Hybrid

Location: Athens • 🇬🇷 Greece

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Job Level

Mid-LevelSenior

Tech Stack

AirflowApacheAWSAzureCloudDockerGoogle Cloud PlatformGrafanaJenkinsKubernetesPrometheusPythonSparkTerraform

About the role

  • Design and Build ML Infrastructure: Create, manage, and scale the infrastructure required for training and deploying our machine learning models.
  • Automate ML Pipelines: Develop and maintain robust CI/CD/CT (Continuous Integration/Continuous Delivery/Continuous Training) pipelines for the full ML lifecycle.
  • Deploy & Serve Models: Implement strategies for deploying models as scalable, reliable services using technologies like containerization (Docker, Kubernetes) and serverless functions.
  • Monitor Model Performance: Establish and manage comprehensive monitoring solutions to track model accuracy, data drift, and system health to ensure our models perform as expected in production.
  • Collaborate Cross-Functionally: Work closely with data scientists to understand model requirements and with software engineers to integrate ML models into our core products.
  • Champion Best Practices: Advocate for and implement MLOps best practices in versioning (data, code, models), testing, and security across the team.

Requirements

  • Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
  • Proven experience in a DevOps, Software Engineering, or MLOps role.
  • Strong programming skills, particularly in Python.
  • Hands-on experience with at least one major cloud platform (AWS, GCP, or Azure) and its ML services (e.g., SageMaker, Vertex AI, Azure ML).
  • Solid experience with containerization (Docker) and orchestration (Kubernetes).
  • Experience building and managing CI/CD pipelines using tools like GitLab CI, GitHub Actions, or Jenkins.
  • A solid understanding of the end-to-end machine learning lifecycle.
  • Preferred
  • Experience with Infrastructure as Code (IaC) tools like Terraform or CloudFormation.
  • Familiarity with MLOps frameworks like MLflow, Kubeflow, or Vertex AI Pipelines.
  • Experience with data processing frameworks such as Apache Spark or data workflow tools like Airflow.
  • Knowledge of model monitoring tools like Prometheus, Grafana, or Evidently AI.
Benefits
  • - Competitive base salary with additional performance incentives.
  • - Coverage under the company’s collective health insurance plan.
  • - Learning and development opportunities (e.g. onboarding, on-the-job training).
  • - Annual training budget.
  • - Hybrid work model & extra personal/flex days and paid volunteer days a year for your favorite cause.
  • - Company sponsored team-bonding events.
  • - Weekly health & wellness activities (e.g. basketball, football, yoga, running), gym discounts, healthy breakfast, snacks and beverages.
  • - Entrepreneurial culture and amazing coworkers!

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills
PythonCI/CDMLOpsInfrastructure as CodeContainerizationOrchestrationData ProcessingModel MonitoringMachine Learning LifecycleVersioning
Soft skills
CollaborationAdvocacyProblem SolvingCommunicationCross-Functional Teamwork
Certifications
Bachelor's degree in Computer ScienceBachelor's degree in EngineeringMLOps certification