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Abhinav Gupta
Junior Machine Learning Engineer specializing in LLMs and applied data science
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Built and shipped multiple production AI systems, including Auto DocGen (LLM-generated OpenAPI docs kept in sync via AST diffs, schema-constrained generation, and CI/CD on Render) and a multimodal sign-language recognition pipeline at USC orchestrated with FastAPI, MediaPipe, and PyTorch. Also partnered with Esri’s non-technical community team to fine-tune an LLaMA-based spam classifier with a review UI, cutting moderation time by 70%.
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Key Strengths
- Built and deployed LLM-powered Auto DocGen to prevent API documentation drift
- Strong LLM reliability/guardrails: JSON schema constraints, function calling, AST diffs, validator layer, approval gates
- Production engineering for AI systems: Docker + CI/CD deployments (Render), caching, batching, retries, rate limiting, incremental updates
- Orchestrated complex multimodal inference pipeline (video → MediaPipe landmarks → PyTorch classifier) with fallbacks and performance optimizations
- Evaluation-driven workflow design: success metrics, unit tests, synthetic edge cases, offline eval + A/B tests + real-traffic logging
- Cross-functional delivery with non-technical stakeholders; built spam-flagging system reducing moderation time by 70%
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