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Sai Teja Challa
Mid-Level AI Engineer specializing in NLP, computer vision, and LLM applications
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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.
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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
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