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Sai Prakash Sasubilli
Mid-level Full-Stack Software Engineer specializing in cloud-native web apps and AI agents
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Full-stack system analyst/programmer at PeakPlay Sports (startup) who built an AI "coach" product end-to-end in ~2 months, using a LangGraph-orchestrated multi-agent architecture with a FastAPI backend. Shipped production RAG grounded in athlete history (OpenAI embeddings + vector store) with guardrails and a structured eval loop (golden set + LLM-judge + human review) to improve engagement and reduce hallucinations.
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Key Strengths
- Owned end-to-end build from architecture through deployment in a startup environment
- Designed multi-agent LLM coaching system with parallel orchestration and conditional execution
- Balanced latency vs response cohesion; improved synthesis via head-coach prompt/architecture changes
- Improved user engagement by restructuring outputs into prioritized action items and table-based plans
- Built production retrieval layer grounding outputs in athlete history using embeddings + vector search
- Implemented LLM guardrails (relevance thresholding, source-attribution prompting) to reduce hallucinations
- Created evaluation loop with curated golden set (40–50 cases), automated scoring, and weekly human review
- Used eval findings to prevent regressions (ensured synthesis includes points from each coach, incl. biomechanics)
- Built normalization and reliability layer for messy real-world inputs with graceful failure and retries
- Implemented observability to trace raw vs normalized inputs, node execution, and report success/failure
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