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Dhanush Kukatla
Mid-level Software Engineer specializing in AI RAG systems and full-stack cloud applications
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About
AI/LLM engineer who shipped a production RAG-based knowledge assistant at SparkPlug serving 10,000+ daily users, streaming GPT-4 answers with inline citations over WebSockets. Demonstrated measurable impact (support resolution time cut 18→12 minutes; retrieval precision +~20%) and strong production rigor across ingestion, monitoring/alerting, evaluation, and messy ERP-style data integration with validation, RBAC, and idempotent operations.
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Key Strengths
- Shipped production LLM knowledge assistant used by 10,000+ daily users
- Reduced average support resolution time from 18 minutes to 12 minutes
- Improved retrieval precision ~20% via sliding-window chunking + semantic deduplication
- End-to-end RAG productionization (ingestion, embeddings, vector store, streaming responses with citations)
- Designs robust failure-mode handling (rate limits, transient errors, retries/timeouts, fallbacks)
- Architects multi-step agent workflows with tool calling, structured outputs, and stateful orchestration
- Builds evaluation + monitoring loops (offline relevance benchmarks + online KPIs) reducing MTTD to <15 minutes
- Strong data-quality/ERP integration practices (validation, normalization, RBAC, idempotency, safe retries)
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