Cloud Lead Engineer- Azure

2 weeks ago

Delhi, India Dailoqa Full time

Years of experience : 10 - 15 yrsLocation : NoidaJoin us as Cloud Lead Engineer- Azure at Dailoqa , where you will be responsible for operationalizing cutting-edge machine learning and generative AI solutions, ensuring scalable, secure, and efficient deployment across infrastructure. You will work closely with data scientists, ML engineers, and business stakeholders to build and maintain robust MLOps pipelines, enabling rapid experimentation and reliable production implementation of AI models, including LLMs and real-time analytics systems.To be successful as Cloud Engineer you should have experience with:Cloud sourcing, networks, VMs, performance, scaling, availability, storage, security, access managementDeep expertise in Azure cloud platformsStrong 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 MLStrong 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 MonitorOptimize performance and resource utilization of AI workloads including GPU-based training/inferenceExperience 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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