Designed, deployed, and optimized machine learning models and pipelines, improving model performance, scalability, and automation across multiple environments.

Implemented CI/CD pipelines with Jenkins and GitLab to automate model deployment across 3 environments, reducing manual errors by 40% and improving model F1 scores by 18% through tuning and validation techniques.

Spearheaded end-to-end model monitoring with Grafana and Prometheus, enabling early detection and rapid mitigation of performance degradation, which led to increased model uptime across critical business services.

Helped improve how we deployed and monitored machine learning models, making the process smoother and more reliable for the team.

Engineered real-time monitoring pipelines via Prometheus and MLflow, surfacing over 15 key performance indicators and slashing triage overhead by 60% through intelligent alerting on latency deviations and model drift.

Orchestrated A/B testing infrastructure for live model benchmarking, empowering stakeholders with rapid, evidence-backed insights and shortening model rollout cycles.

Worked on cleaning and understanding customer data to find patterns that helped the team make better decisions.

Architected feature extraction pipelines and leveraged advanced statistical methods on unstructured customer data to surface actionable patterns, driving a 20% boost in decision-making efficiency through predictive insight generation.