ΠΎΠ±Π½ΠΎΠ²Π»Π΅Π½ΠΎ 8 Π΄Π½Π΅ΠΉ Π½Π°Π·Π°Π΄
MLOps Engineer (AI/MLOps)
ΠΠ°ΠΊΠ°Π½ΡΠΈΡ ΠΈΠ· Hirify Global, ΡΠΏΠΈΡΠΊΠ° ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΡΡ
tech-ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΉΠΠ»Ρ ΠΌΡΡΡΠ° ΠΈ ΠΎΡΠΊΠ»ΠΈΠΊΠ° Π½ΡΠΆΠ΅Π½ Plus
ΠΡΡΡ & Π‘ΠΎΠΏΡΠΎΠ²ΠΎΠ΄
ΠΠ»Ρ ΠΌΡΡΡΠ° Ρ ΡΡΠΎΠΉ Π²Π°ΠΊΠ°Π½ΡΠΈΠ΅ΠΉ Π½ΡΠΆΠ΅Π½ Plus
ΠΠΏΠΈΡΠ°Π½ΠΈΠ΅ Π²Π°ΠΊΠ°Π½ΡΠΈΠΈ
Π’Π΅ΠΊΡΡ:
TL;DR
MLOps Engineer (AI/MLOps): Bridging Data Science and Infrastructure teams by supporting MLOps tasks and DevOps initiatives, including CI/CD pipeline creation, cloud resource provisioning, and Kubernetes orchestration. Focus on deploying data pipelines, training and managing machine learning models within scalable cloud environments, ensuring high performance, security, and reliability throughout the ML lifecycle.
Location: Hybrid in Greece, with locations in Limassol or Nicosia
Company
operates an engineering organization with Cloud DevOps, Data Science, and infrastructure functions.
What you will do
- Design, implement, and maintain scalable MLOps pipelines on AWS using SageMaker, EC2, EKS, S3, Lambda, and related services.
- Coordinate with the platform team to troubleshoot Kubernetes clusters and orchestrate machine learning models and microservices.
- Develop and maintain CI/CD pipelines for model and application deployment, testing, and monitoring.
- Collaborate with Data Science and DevOps teams throughout the model development lifecycle, from experimentation to production.
- Implement AWS security practices covering network security, data encryption, and role-based access control.
- Monitor, troubleshoot, and optimize ML and data pipelines, including model performance drift monitoring.
Requirements
- Bachelorβs degree in Computer Science, Engineering, or a related field.
- 2+ years of hands-on experience in MLOps, DevOps, or related fields.
- Knowledge of AWS machine learning services, Kubernetes, Docker, Terraform or CloudFormation, and GitLab CI.
- Understanding of machine learning model lifecycles, cloud networking, security, monitoring, and logging.
- Experience with Prometheus, Grafana, CloudWatch, ELK Stack, Python, Bash, and Linux environments.
- Strong problem-solving and communication skills.
Nice to have
- Experience with serverless architectures, event-driven processing, and advanced Kubernetes concepts such as Helm.
- Experience with data engineering pipelines, ETL processes, or big data platforms.
- Experience with TensorFlow, PyTorch, Keras, Kubeflow, SageMaker, Argo Workflows, or Airflow.
Culture & Benefits
- Attractive remuneration package with performance-related reward.
- Intellectually stimulating work environment.
- Continuous personal development and international training opportunities.
Hiring process
- Introductory chat with Talent Acquisition.
- First interview with the future team.
- Final interview.
ΠΡΠ΄ΡΡΠ΅ ΠΎΡΡΠΎΡΠΎΠΆΠ½Ρ: Π΅ΡΠ»ΠΈ ΡΠ°Π±ΠΎΡΠΎΠ΄Π°ΡΠ΅Π»Ρ ΠΏΡΠΎΡΠΈΡ Π²ΠΎΠΉΡΠΈ Π² ΠΈΡ ΡΠΈΡΡΠ΅ΠΌΡ, ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡ iCloud/Google, ΠΏΡΠΈΡΠ»Π°ΡΡ ΠΊΠΎΠ΄/ΠΏΠ°ΡΠΎΠ»Ρ, Π·Π°ΠΏΡΡΡΠΈΡΡ ΠΊΠΎΠ΄/ΠΠ, Π½Π΅ Π΄Π΅Π»Π°ΠΉΡΠ΅ ΡΡΠΎΠ³ΠΎ - ΡΡΠΎ ΠΌΠΎΡΠ΅Π½Π½ΠΈΠΊΠΈ. ΠΠ±ΡΠ·Π°ΡΠ΅Π»ΡΠ½ΠΎ ΠΆΠΌΠΈΡΠ΅ "ΠΠΎΠΆΠ°Π»ΠΎΠ²Π°ΡΡΡΡ" ΠΈΠ»ΠΈ ΠΏΠΈΡΠΈΡΠ΅ Π² ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΡ. ΠΠΎΠ΄ΡΠΎΠ±Π½Π΅Π΅ Π² Π³Π°ΠΉΠ΄Π΅ β