Sai Teja Challa

About

LLM/RAG practitioner who productionized an LLM-driven customer communication and transaction understanding system at PayPal, emphasizing privacy/compliance guardrails and large-scale data normalization. Experienced in real-time debugging of hallucinations via retrieval pipeline tuning and in leading hands-on developer workshops and sales-aligned POCs to drive adoption.

Experience

Education

Key Strengths

• Took an LLM-driven customer communication/transaction understanding system from prototype to production at PayPal
• Productionization focus: privacy/compliance guardrails (PII masking, output validation, fallback logic) plus monitoring
• Strong debugging/incident-style diagnosis for LLM workflows using logs, traces, and prompt versioning
• RAG and retrieval tuning expertise to reduce hallucinations (chunking, relevance thresholds, ingestion improvements)
• Cross-functional alignment with support, engineering, and stakeholders on success metrics (accuracy, latency, privacy, compliance)
• Effective technical demos/workshops with hands-on live debugging; enabled reuse of patterns by multiple teams
• Partners with sales/customer-facing teams on discovery, demos, POCs, and onboarding to drive adoption
• Built and deployed production LLM chatbot at PayPal for customer support
• Improved response accuracy by 30% and customer satisfaction by 25%
• Scaled preprocessing/normalization for millions of noisy, domain-specific records
• Designed hybrid retrieval + generation approach for context-aware responses
• Strong LLM workflow reliability practices (unit/integration/regression testing, golden sets, A/B tests)
• Systematic debugging and iteration using end-to-end logs/metrics plus targeted fixes (retrieval, prompts, retries/fallbacks)
• Effective collaboration with non-technical stakeholders using visual workflows and concrete examples