Core Competency
MLOps & AI Orchestration
Bridging the gap between data science and operations by automating machine learning lifecycles, tracking experiments with MLflow, and orchestrating complex pipelines using Apache Airflow.
DAG Workflow Management
Authoring, scheduling, and monitoring multi-stage data pipelines as Directed Acyclic Graphs (DAGs) in Python to enforce explicit upstream and downstream dependencies.
Model Lineage Tracking
Logging parameters, code versions, metrics, and output artifacts for every single machine learning training run to ensure absolute reproducibility and compliance auditing.
Automated Model Drift Alerts
Deploying continuous monitoring loops to evaluate production model accuracy, automatically triggering retraining pipelines when performance slips below baseline metrics.