name: "Bharghava Ram Vemuri"
role: "LLM & GenAI Engineer"
experience: "2+ years shipping production GenAI"
focus:
- "RAG pipelines → +45% relevance gain (LangSmith verified)"
- "Multi-agent orchestration with AutoGen / LangChain"
- "Prompt evaluation + LLM observability at scale"
deployed_to: ["AWS SageMaker", "Azure ML", "GCP Vertex AI"]
mantra: "Ship reliable LLMs, measure everything, ship again."
+ LLM-as-a-judge hallucination detection
+ Cross-model drift analytics
+ Configurable cost/latency alerting |
🛡️ PromptGuard
+ Validation cycle: 3 weeks → 4 days
+ +45% output reliability
+ Onboarded 8+ developers |
+ -60% manual steps across 200+ runs
+ P95 latency < 4s under load
+ 1–3 hrs saved per workflow |
+ -55% report generation time
+ ReAct query routing
+ End-to-end GenAI pipeline |
+ +35% answer relevance -28% query response time
+ 87% CoT accuracy 20+ concurrent users on AWS |
|
| 🎯 Metric | 📈 Result |
|---|---|
| Answer relevance gain across RAG pipelines | +45% (LangSmith verified) |
| Manual workflow steps eliminated | −60% across 200+ runs |
| Production availability (5-month tenure) | 99.5% |
| Prompt validation cycle compression | 3 weeks → 4 days |
| Predictive accuracy boost across 10+ ML models | +18% |
| Adversarial test cases stress-tested | 500+ |
🎓 B.Tech — Computer Science Engineering, SVIT, Telangana (2019 – 2023)
"The best way to predict the future of AI is to ship it — instrumented, evaluated, and reliable."