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Charith Kandula
Mid-level Conversational AI Engineer specializing in enterprise chatbots and workflow automation
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Built a production LLM/RAG document extraction and game/quiz content workflow using LLaMA 2, LangChain/LangGraph, and FAISS, achieving ~94% accuracy and reducing turnaround from hours to minutes. Demonstrates strong applied MLOps/orchestration (CI/CD, MLflow, Databricks/PySpark), robust handling of noisy/variable document layouts (layout chunking + OCR fallbacks), and practical reliability practices (human-in-the-loop routing, drift monitoring, A/B testing).
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Key Strengths
- Built and deployed production RAG-based document extraction system (LLaMA 2 + LangChain + FAISS)
- Improved processing time from hours to minutes while achieving ~94% accuracy
- Handled 500+ document types via layout-aware chunking and OCR fallback/validation for noisy scans
- Addressed imbalanced data and model drift using fine-tuning, active learning, monitoring, augmentation, and semi-supervised learning
- Designed reliable agent/workflow evaluation with unit tests, synthetic simulations, A/B tests, and metrics (accuracy/F1/latency/error rates) plus drift monitoring
- Implemented LangGraph branching to route low-confidence outputs to validation tools or human review
- Effective collaboration with non-technical stakeholders by translating requirements into measurable metrics (accuracy, turnaround time, auditability)
- Designs scalable backend architectures with clear layering (API, data, security, observability)
- API reliability focus: explicit contracts (OpenAPI), stable schemas, and edge-case handling
- Migration/refactor execution using strangler pattern, feature flags, and incremental rollout
- Strong data integrity practices: constraints, transactions, dual read/write, restartable backfills
- Improves robustness by identifying failure modes (duplicate job creation) and implementing idempotent endpoints
- Production security implementation with JWT/OAuth and multi-tenant access patterns
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2 publications
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