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Lead Cloud Engineer

Lead Cloud Engineer

Dailoqa

0 years

0 Lacs

noida uttar pradesh india

Posted:2 days ago| Platform:

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Skills Required

cutting learning ai deployment data ml experimentation analytics sourcing networks scaling storage security management aws azure containerization orchestration docker kubernetes github jenkins devops scripting python mlflow sagemaker vertex design code terraform automate provisioning model compliance monitoring logging training databricks collaborative development interpretability leadership communication collaboration governance retrieval

Work Mode

On-site

Job Type

Full Time

Job Description

About the Open Position

Years of exp

Location

Cloud Engineer

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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