GCP Cloud Engineer
Chennai, Tamil Nadu, India
INR 2500K-4500K (estimate) Senior-level Full Time
Tasks
- Apply code testing and documentation best practices
- Automate data ingestion and feature engineering
- Build CI CD pipelines for AI deployments
- Build LLM-powered applications
- Build reproducible experiments
- Containerize AI workloads with Docker
- Deploy AI workloads to Cloud Run
- Deploy AI workloads to GKE
- Design AI agent pipelines
- Enforce IAM policies
- Implement blue-green/canary deployments
- Implement data lineage tracking
- Implement rag pipelines
- Integrate vector databases for embeddings
- Manage AI agent model lineage and versioning
- Manage BigQuery datasets
- Manage GCP networking and security
- Manage GIT/source control
- Manage Google Cloud Storage datasets and artifacts
- Manage pipeline versioning
- Monitor model performance drift and data quality
- Optimize GCP resource utilization and cost
- Orchestrate AI training and deployment workflows
- Package reusable components with PyPI
- Provision infrastructure using infrastructure-as-code
- Set up alerting dashboards
- Troubleshoot infrastructure issues
- Write production AI agent services in Python
Perks/Benefits
- N/A
Skills/Tech-stack
AI Pipelines | BigQuery | Blue-Green Deployment | Blue/green | CI/CD | Canary Deployment | Cloud Build | Cloud Run | Cloud Run Jobs | Cloud Storage | Cloud platform | Data Lineage | Data Quality | Docker | Drift Detection | Embeddings | Firewalls | Git | Google Cloud | Google Cloud Platform | Google Cloud Storage | Google Kubernetes | Google Kubernetes Engine | IAM | Infrastructure as Code | Kubernetes | Kubernetes Engine | LLM | Load balancers | Model Drift | Model Drift Detection | Monitoring | Observability | PyPI | Python | RAG | Retrieval-Augmented Generation | Terraform | VPC | Vector Databases | Vertex AI | Vertex AI pipelines | “as-code”
Education
N/A
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