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QCS Dc Labs

Key Responsibilities:

Deliver engaging and interactive training sessions (24 hours total) based on structured modules.
Teach integration of monitoring, logging, and observability tools with machine learning.
Guide learners in real-time anomaly detection, incident management, root cause analysis, and predictive scaling.
Support learners in deploying tools like Prometheus, Grafana, OpenTelemetry , Neo4j, Falco, and KEDA.
Conduct hands-on labs using LangChain , Ollama , Prophet, and other AI/ML frameworks.
Help participants set up smart workflows for alert classification and routing using open-source stacks.
Prepare learners to handle security, threat detection, and runtime anomaly classification using LLMs.
Provide post-training support and mentorship when necessary.

Skill : -

Kubernetes: Minikube , Helm, kubectl , HPA, KEDA
Observability & Monitoring: Prometheus, Grafana, OpenTelemetry , ELK Stack, FluentBit
AI/ML: Python, scikit-learn, Prophet, LangChain , Ollama (LLMs)
Security Tools: Falco, KubeArmor , Sysdig Secure
Dev Tools: Docker, VSCode , Jupyter Notebooks
LLMs & Automation: LangChain , Neo4j, GPT-based explanation tools, Slack Webhooks