Designing scalable machine learning platforms and production AI systems
Years Engineering
Production Systems
Cloud Architectures
Platforms Built
Designing end‑to‑end ML systems covering data pipelines, training workflows, deployment and monitoring.
Building internal ML platforms enabling data scientists to ship models safely.
Architecting scalable cloud‑native services for ML workloads.
Mentoring engineers and guiding long‑term platform architecture.
Streaming architecture enabling asynchronous inference triggered by real‑time events.
CI/CD pipelines enabling model training, validation, promotion and rollback.
Monitoring systems tracking model drift, prediction health and latency.
Example ML platform architecture covering data ingestion, feature engineering, training pipelines and inference APIs.
Design principles for reliable ML platforms.
Designing scalable feature engineering systems.
Lessons from production ML deployments.