Ahmed Sadaqat

About

ML/AI engineering leader who has owned end-to-end production systems from experimentation through deployment, monitoring, and iteration at meaningful scale. They describe running a 1M+ records/day prediction platform with 99.9% availability, shipping a RAG-based conversational AI feature for 50,000 active users, and consistently improving precision, latency, reliability, and cost with measurable business impact.

Key Strengths

• Owned end-to-end production ML systems at scale
• Scaled ML platform processing 1M+ records daily with 99.9% availability
• Improved model precision by 32% through hyperparameter tuning and feature engineering
• Reduced release failures by 35% via Docker/Kubernetes deployment standardization
• Cut data latency by 40% through ETL pipeline restructuring
• Maintained inference latency under 200ms
• Reduced compute costs by 25% with Redis caching
• Built monitoring for model drift and automated retraining
• Reduced production regressions by 30% with automated ML workflow testing
• Shipped enterprise RAG feature for grounded conversational AI
• Balanced latency, cost, quality, and safety tradeoffs in production AI systems
• Drove 20% increase in user engagement
• Supported customer-facing systems serving 50,000 active users
• Led development team on end-to-end ML prediction system

Experience

Education

Skills

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