Descrierea postului
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 ianuarie 2026
La distanță
De la 2 ani
Full-time
Nu contează
Remote
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
Cunoașterea limbilor:
Română Fluent
Engleză Fluent
Adresa:
La distanță
Data actualizării:
17 ianuarie 2026
Aplicat!
Candidații înregistrați pe site primesc mai des răspunsuri de la angajatori și pot comunica direct cu ei prin CHAT.