Shrikant Agrawal

About

I work across the full lifecycle of data and machine learning systems — from ingestion and processing to model training, deployment, and monitoring. My work emphasizes production-first design, fault tolerance, and long-term maintainability.

I regularly build and operate event-driven architectures, streaming inference systems, and automated ML pipelines using modern DataOps, MLOps, and GenAIOps practices.

Core Expertise

Case Studies

• Real-Time Fraud Detection Platform (Kafka + Spark + MLflow)
• End-to-End MLOps Pipeline with Airflow
• Streaming Inference Architecture (Low Latency)
• GenAI RAG System Design & Deployment

Project Code & Repositories

• Kafka + Spark Streaming Examples
• MLOps Reference Architecture
• Airflow DAG Patterns
• Inference Service Templates

Commands, Tips & Engineering Notes

• Kafka Debugging & Operations Commands
• Spark Streaming Performance Tuning
• Airflow Production Best Practices
• MLOps Failure Patterns & Fixes