Описание вакансии
MUST HAVE
- Experience: 8+ years in Cloud Architecture, with experience on MLOps or productionizing ML models.
- Azure Expertise: Deep knowledge of Azure services including AKS, Azure Machine Learning, Azure DevOps, Key Vault, and CosmosDB.
Technical Stack:
- Strong proficiency in Python (specifically for ML and backend services).
- Experience with MLOps tools (e.g., MLflow, Kubeflow, or DVC).
- Containerization expertise (Docker, Kubernetes, Helm).
- CI/CD: Proven track record of building automated deployment pipelines for both software and models.
- Architecture: Strong understanding of microservices, event-driven architecture, and REST/gRPC API design
NICE TO HAVE
- Experience in the ERP sector.
- Knowledge of LLM orchestration frameworks (e.g., LangChain, Semantic Kernel).
- Experience with vector databases and RAG (Retrieval-Augmented Generation) architectures.
RESPONSIBILITIES:
- Architectural Leadership: Design and implement robust, scalable, and secure cloud-native architectures on Azure that support both high- concurrency ERP modules and intensive ML workloads.
- MLOps Lifecycle Management: Build and maintain automated end-to-end pipelines for ML model development, training, testing, deployment, and monitoring (CI/CD/CT).
- Infrastructure as Code (IaC): Lead the automation of environment provisioning using Terraform or Bicep, ensuring consistency across development, staging, and production.
- Scalability & Orchestration: Optimize our Kubernetes (AKS) strategy to handle the dynamic scaling requirements of AI agents and microservices.
- Data Strategy: Collaborate with data engineers to design high-performance data lakes and streaming architectures that fuel the AI Studio.
- Security & Compliance: Ensure multi-tenant isolation and data privacy standards are strictly met across the entire cloud and ML stack.
- Cross-Functional Collaboration: Act as the technical bridge between DevOps, Data Science, and Software Engineering teams to streamline the path from model prototype to production
- Experience: 8+ years in Cloud Architecture, with experience on MLOps or productionizing ML models.
- Azure Expertise: Deep knowledge of Azure services including AKS, Azure Machine Learning, Azure DevOps, Key Vault, and CosmosDB.
Technical Stack:
- Strong proficiency in Python (specifically for ML and backend services).
- Experience with MLOps tools (e.g., MLflow, Kubeflow, or DVC).
- Containerization expertise (Docker, Kubernetes, Helm).
- CI/CD: Proven track record of building automated deployment pipelines for both software and models.
- Architecture: Strong understanding of microservices, event-driven architecture, and REST/gRPC API design
NICE TO HAVE
- Experience in the ERP sector.
- Knowledge of LLM orchestration frameworks (e.g., LangChain, Semantic Kernel).
- Experience with vector databases and RAG (Retrieval-Augmented Generation) architectures.
RESPONSIBILITIES:
- Architectural Leadership: Design and implement robust, scalable, and secure cloud-native architectures on Azure that support both high- concurrency ERP modules and intensive ML workloads.
- MLOps Lifecycle Management: Build and maintain automated end-to-end pipelines for ML model development, training, testing, deployment, and monitoring (CI/CD/CT).
- Infrastructure as Code (IaC): Lead the automation of environment provisioning using Terraform or Bicep, ensuring consistency across development, staging, and production.
- Scalability & Orchestration: Optimize our Kubernetes (AKS) strategy to handle the dynamic scaling requirements of AI agents and microservices.
- Data Strategy: Collaborate with data engineers to design high-performance data lakes and streaming architectures that fuel the AI Studio.
- Security & Compliance: Ensure multi-tenant isolation and data privacy standards are strictly met across the entire cloud and ML stack.
- Cross-Functional Collaboration: Act as the technical bridge between DevOps, Data Science, and Software Engineering teams to streamline the path from model prototype to production
Cloud Architect
17 января 2026
Удалённо
От 2 лет
Полный день
Не имеет значение
Удалённо
MUST HAVE
- Experience: 8+ years in Cloud Architecture, with experience on MLOps or productionizing ML models.
- Azure Expertise: Deep knowledge of Azure services including AKS, Azure Machine Learning, Azure DevOps, Key Vault, and CosmosDB.
Technical Stack:
- Strong proficiency in Python (specifically for ML and backend services).
- Experience with MLOps tools (e.g., MLflow, Kubeflow, or DVC).
- Containerization expertise (Docker, Kubernetes, Helm).
- CI/CD: Proven track record of building automated deployment pipelines for both software and models.
- Architecture: Strong understanding of microservices, event-driven architecture, and REST/gRPC API design
NICE TO HAVE
- Experience in the ERP sector.
- Knowledge of LLM orchestration frameworks (e.g., LangChain, Semantic Kernel).
- Experience with vector databases and RAG (Retrieval-Augmented Generation) architectures.
RESPONSIBILITIES:
- Architectural Leadership: Design and implement robust, scalable, and secure cloud-native architectures on Azure that support both high- concurrency ERP modules and intensive ML workloads.
- MLOps Lifecycle Management: Build and maintain automated end-to-end pipelines for ML model development, training, testing, deployment, and monitoring (CI/CD/CT).
- Infrastructure as Code (IaC): Lead the automation of environment provisioning using Terraform or Bicep, ensuring consistency across development, staging, and production.
- Scalability & Orchestration: Optimize our Kubernetes (AKS) strategy to handle the dynamic scaling requirements of AI agents and microservices.
- Data Strategy: Collaborate with data engineers to design high-performance data lakes and streaming architectures that fuel the AI Studio.
- Security & Compliance: Ensure multi-tenant isolation and data privacy standards are strictly met across the entire cloud and ML stack.
- Cross-Functional Collaboration: Act as the technical bridge between DevOps, Data Science, and Software Engineering teams to streamline the path from model prototype to production
- Experience: 8+ years in Cloud Architecture, with experience on MLOps or productionizing ML models.
- Azure Expertise: Deep knowledge of Azure services including AKS, Azure Machine Learning, Azure DevOps, Key Vault, and CosmosDB.
Technical Stack:
- Strong proficiency in Python (specifically for ML and backend services).
- Experience with MLOps tools (e.g., MLflow, Kubeflow, or DVC).
- Containerization expertise (Docker, Kubernetes, Helm).
- CI/CD: Proven track record of building automated deployment pipelines for both software and models.
- Architecture: Strong understanding of microservices, event-driven architecture, and REST/gRPC API design
NICE TO HAVE
- Experience in the ERP sector.
- Knowledge of LLM orchestration frameworks (e.g., LangChain, Semantic Kernel).
- Experience with vector databases and RAG (Retrieval-Augmented Generation) architectures.
RESPONSIBILITIES:
- Architectural Leadership: Design and implement robust, scalable, and secure cloud-native architectures on Azure that support both high- concurrency ERP modules and intensive ML workloads.
- MLOps Lifecycle Management: Build and maintain automated end-to-end pipelines for ML model development, training, testing, deployment, and monitoring (CI/CD/CT).
- Infrastructure as Code (IaC): Lead the automation of environment provisioning using Terraform or Bicep, ensuring consistency across development, staging, and production.
- Scalability & Orchestration: Optimize our Kubernetes (AKS) strategy to handle the dynamic scaling requirements of AI agents and microservices.
- Data Strategy: Collaborate with data engineers to design high-performance data lakes and streaming architectures that fuel the AI Studio.
- Security & Compliance: Ensure multi-tenant isolation and data privacy standards are strictly met across the entire cloud and ML stack.
- Cross-Functional Collaboration: Act as the technical bridge between DevOps, Data Science, and Software Engineering teams to streamline the path from model prototype to production
Знание языков:
Румынский Продвинутый
Английский Продвинутый
Адрес:
Удалённо
Дата актуализации:
17 января 2026
Отклик отправлен!
Зарегистрированные на сайте кандидаты чаще получают ответы от работодателей и могут напрямую общаться с ними в ЧАТЕ.