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Cloud Engineer Lead

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Cloud Engineer Lead


To be successful as Cloud Engineer you should have experience with:


  • Cloud sourcing, networks, VMs, performance, scaling, availability, storage, security, access management      
  •  Deep expertise in one or more cloud platforms: AWS, Azure, GCP 
  • Strong experience in containerization and orchestration (Docker, Kubernetes, Helm) 
  • Familiarity with CI/CD tools: GitHub Actions, Jenkins, Azure DevOps, ArgoCD, etc. 
  • Proficiency in scripting languages (Python, Bash, PowerShell) 
  • Knowledge of MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML 
  • Strong understanding of DevOps principles applied to ML workflows. 


Key Responsibilities may include:


  • Design and implement scalable, cost-optimized, and secure infrastructure for AI-driven platforms. 
  • Implement infrastructure as code using tools like Terraform, ARM, or Cloud Formation. 
  • Automate infrastructure provisioning, CI/CD pipelines, and model deployment workflows. 
  • Ensure version control, repeatability, and compliance across all infrastructure components. 
  • Set up monitoring, logging, and alerting frameworks using tools like Prometheus, Grafana, ELK, or Azure Monitor. 
  • Optimize performance and resource utilization of AI workloads including GPU-based training/inference 
  • Experience with Snowflake, Databricks for collaborative ML development and scalable data processing. 
  • Understanding model interpretability, responsible AI, and governance. 
  • Contributions to open-source MLOps tools or communities. 
  • Strong leadership, communication, and cross-functional collaboration skills. 
  • Knowledge of data privacy, model governance, and regulatory compliance in AI systems. 
  • Exposure to LangChain, Vector DBs (e. g. , FAISS, Pinecone), and retrieval-augmented generation (RAG) pipelines. 

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