Fahad Altaf

About

End-to-end engineer focused on AI-native enterprise systems, including a production generative knowledge platform using RAG + semantic search over internal documentation (React, Python/Flask, GPU-hosted NLP models, Pinecone) with strong CI/CD and observability. Reports concrete outcomes including 40% faster knowledge access and ~75% employee adoption, and has led incremental cloud-native modernization using feature flags, parallel runs, canary releases, and regression testing.

Experience

Education

Key Strengths

• Owned and architected an enterprise generative knowledge platform end-to-end (frontend, backend, data, infra)
• Production RAG + semantic search implementations using vector DBs (Pinecone, ChromaDB) and transformer embeddings
• Built CI/CD with Jenkins enabling continuous deployment with zero downtime
• Implemented production observability (CloudWatch, logging, Grafana dashboards) tracking response time, error rates, and query accuracy
• Delivered measurable adoption and productivity impact: 40% faster knowledge access; ~75% employee active usage; ~5% reduction in manual effort
• Legacy modernization to cloud-native via incremental refactors with parallel run, feature flags, automated regression tests, canary releases, and load testing
• Handled cross-team delivery with minimal oversight using prioritization frameworks, daily standups, time-blocking, and short sprints
• Designed evaluation/guardrails for LLM systems: continuous evaluation, human-in-the-loop feedback, confidence scoring, and sensitive-data human validation