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jaswanth mada
Mid-level Applied AI/ML Engineer specializing in LLMs, RAG, and fraud/anomaly detection
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Built and productionized an internal LLM-powered document Q&A system at Morgan Stanley using a LangChain-based RAG pipeline (FAISS + OpenAI) with AWS ingestion (S3/Lambda), handling 100k+ pages and cutting lookup time ~35% while keeping responses under 3 seconds. Strong on reliability: automated evals/CI (pytest + GitHub Actions), CloudWatch monitoring, drift detection (prompt drift and fraud-model drift), and security controls (IAM + app-level authorization) in a financial-services environment.
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Key Strengths
- Built and deployed internal LLM document Q&A/RAG system at Morgan Stanley (100,000+ pages ingested)
- Reduced end-user document lookup time by ~35%
- Improved grounded/citable responses; reduced partially grounded responses by ~25% via chunking + retrieval depth + prompt/citation tuning
- Designed structure-aware chunking strategies (token size/overlap tuning; markdown structural splitting) to improve retrieval consistency
- Implemented secure access control in RAG pipeline using AWS IAM plus application-level authorization
- Maintained sub-3-second latency while managing inference cost through CloudWatch instrumentation and tuning
- Established automated evaluation and CI gating (pytest + GitHub Actions) for prompt/retrieval changes before deployment
- Proactively detected and mitigated prompt drift after OpenAI API updates using versioned prompts and automated response-structure tests
- Detected model drift in fraud classification using KS and precision-recall monitoring and triggered retraining before production impact
- Effective collaboration with non-technical stakeholders (research/compliance) to shape requirements and validate output quality
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