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Rajdeep Savani
Mid-level Software Engineer specializing in full-stack agentic AI
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About
Built a production-grade agentic document intake system that converts PDFs into structured records with strict schema validation, confidence-based retries, and a human review UI. Demonstrates strong practical judgment around making LLM systems reliable in enterprise workflows, including custom orchestration, observability, and continuous evals rather than relying on off-the-shelf abstractions.
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Key Strengths
- built agentic document intake pipelines
- strong AI output validation and reliability design
- human-in-the-loop workflow architecture
- schema-driven extraction systems
- debugging and reducing LLM extraction failures
- production monitoring and observability for agent systems
- custom orchestration over framework abstraction
- evaluation-driven prompt and rule improvement
- multi-language backend engineering across TypeScript, Rust, and Ruby
- quality-focused review process for AI-assisted code
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