I will be data scientist and mlops engineer using python

MLOps Engineer | Data Scientist | 2+ Years Experience

My expertise are

CI/CD: GitHub Actions, Jenkins, ArgoCD - Reduced release cycles 70%, deployment failures 85%

Containerization: Docker, Kubernetes (EKS/AKS/GKE), HPA, Istio, Helm - 40% smaller images, 99.9% availability

ML Pipelines: Kubeflow, MLflow, Airflow, Feast, DVC, ZenML - 85% less manual work, 50% faster dev

️ Cloud: AWS (SageMaker, ECR, EKS, Lambda, API Gateway), Azure ML, GCP Vertex AI, Terraform

Monitoring: Prometheus, Grafana, Evidently AI, DeepChecks, WhyLogs, PagerDuty - 75% faster MTTD

Data Quality: Great Expectations, Pandera, Pydantic - 60% fewer data issues, 15+ expectation suites

NLP & LLMs: PyTorch, Hugging Face, LangChain, RAG, Fine-Tuning, LLaMA, VLLM - 89% sentiment accuracy

Models: Churn (85% precision, 15% retention lift), XGBoost (80% R²), 10K+ daily predictions

I build production-grade, scalable, end-to-end MLOps pipelines with experiment tracking, model versioning, automated retraining, and drift detection. Let's deploy your AI/ML models at scale!

Tech Stack: Python | SQL | TensorFlow | Scikit-learn | FastAPI | Redis | PostgreSQL | Pytest | Git | Linux | Bash