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Srichandan Kota
Senior Full-Stack AI Engineer specializing in Generative AI and FinTech
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About
Backend engineer who built and owned an AI-powered financial research product end-to-end, using a typed NestJS/GraphQL backend with LangGraph-style agent routing to produce sourced, structured financial analysis. Emphasizes finance-grade correctness (Zod validation, metric registries, unit/empty-result guardrails) while keeping latency low via batching, caching, and fast token streaming, and has led incremental migrations using strangler/feature-flag/shadow traffic patterns.
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Key Strengths
- End-to-end ownership of an AI-powered financial research backend
- Designs for correctness/guardrails in high-stakes finance (schema validation, metric whitelisting, structured outputs)
- Low-latency API design (GraphQL batching, caching, fewer round trips, fast token streaming)
- Scalable agent/workflow architecture with typed tool calls and sub-agent routing
- Safe incremental migrations (strangler pattern, feature flags, shadow mode)
- Strong API reliability/security practices (timeouts/retries, JWT, RBAC, rate limiting, idempotency, queues)
- Identifies and mitigates silent failure modes (unit mismatches, empty-result summarization) via registries and normalization
- Built and shipped production AI-powered financial research backend end-to-end (API + data + orchestration)
- Strong reliability focus: prevents confident-wrong LLM outputs via registry verification and schema validation
- Designs agent systems with strict planning (JSON) vs execution separation; tools never directly controlled by the model
- Robust error handling with explicit success/retryable/hard-failure states and predefined fallbacks
- Operational rigor: run-level audit trails, step traces, replayability, and monitoring via validation/fallback rates
- Full-stack ownership of an agentic visual workflow builder (UI serialization to JSON graph + backend compilation/execution)
- Solid testing discipline for AI workflows (unit/integration/golden-path) using pytest and deterministic mocks
- Relational modeling for versioned workflows and runs; performance tuning via compound indexes and join reduction
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