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Prasanth Goli
Mid-level Data Scientist specializing in Generative AI and LLM production systems
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Built and deployed a production LLM-powered workflow assistant that automated internal marketing/production business tasks (document summarization, repeated Q&A, status updates). Demonstrates end-to-end applied LLM engineering: modular RAG architecture, hallucination/latency mitigation, automated evals to prevent prompt regressions, and Azure-based orchestration (Functions/Logic Apps) with monitoring and controlled rollouts.
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Key Strengths
- Built and deployed an LLM-powered workflow assistant to production for marketing/production operations automation
- Designed modular LLM system architecture (retrieval layer, orchestration layer, API layer)
- Improved reliability by grounding outputs with retrieval and enforcing "don't know" when context is missing
- Reduced latency via context reduction, caching common requests, and workflow optimization
- Prevented prompt-change regressions with automated evaluation tests before deployment
- Production observability: logging/monitoring to trace failures quickly
- Orchestrated multi-step AI workflows with isolated steps, structured outputs, retries, branching logic, and timeouts
- Strong stakeholder collaboration: translated non-technical needs into measurable AI workflow goals and iterated via demos/feedback
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