data engineering , Data Cleaning, Kafka, MLops, Neo4j, Docker, Spark, Kubernetes, Python, LangChain, Airflow, Generative AI, MLflow, LangGraph, Data Procurement, Knowledge Graph Engineering

Github, Incident Response, Linux Networking, Docker, Terraform, Gitlab, Azure DevOps, Cloudformation, Jenkins, Dashboards, Ansible, metrics, Kubernetes, security basics, alerting, artifact signing, ML GenAI delivery, observability, tracing, model packaging, DevOps practices, secrets management, centralized logging, monitoring hooks, Infrastructure-as-Code, dependency scanning

Flask, FastAPI, Rest Apis, Python, Generative AI, CI CD CT pipelines, MLOps principles

Gcp, Python Programming, Feature engineering pipelines, Model serving infrastructure, Experiment tracking, Container orchestration, ML pipeline design, Dataset versioning, Containerisation

MLops, Gcp, Vertex AI, Model Monitoring, Model Drift Detection

Prometheus, Grafana, Elk Stack, Gitlab, Artifactory, Aws, Gitflow

Cloud Platforms (AWS, Azure), MLops, Machine Learning, Python, Kubeflow, ML Pipeline, ML Deployment

Kubernetes, Python, Dockers, Devops, Git, Azure ML, CI CD pipelines, MLOps models re-training labelling monitoring, Cloud AI Services, Generative AI architectures and solutions, ML models optimization, applications industrialization