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YAKKALI PAVAN
Mid-level Machine Learning & Generative AI Engineer specializing in NLP, CV, and RAG systems
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Built and deployed a production LLM-powered RAG document intelligence system used by non-technical enterprise stakeholders, cutting document search time by 40%+ while improving answer consistency. Demonstrates strong MLOps/data workflow orchestration (Airflow, AWS Step Functions, managed schedulers across GCP/Azure) and a metrics-driven approach to reliability, evaluation, and cost/latency optimization with guardrails and observability.
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Key Strengths
- Built and deployed production LLM RAG system for enterprise document Q&A
- Reduced document search time by 40%+ for business users/non-technical stakeholders
- Strong hallucination/accuracy mitigation using grounded context and low-confidence rejection
- Latency and cost optimization via caching, batch retrieval, and prompt optimization
- Deep workflow orchestration experience (Airflow + cloud-native tools) across end-to-end data/ML pipelines
- Reliability/observability at scale: structured logging, idempotency, exponential backoff retries, SLA monitoring, and alerting tied to data quality
- Rigorous agent/workflow evaluation: offline + synthetic tests, regression testing, prompt versioning, and online monitoring with A/B testing
- Effective collaboration with non-technical stakeholders; builds trust with citations, confidence indicators, and live demos on real data
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