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Aravind Mohan
Junior Software Engineer specializing in AI agents and backend systems
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About
Backend/AI workflow engineer who built a production event-personalization service (FastAPI + AWS Lambda) and solved real-world reliability/latency issues with deterministic routing, caching, and query/index optimization. Also built an end-to-end Gmail-based job application tracking agent using a lightweight RAG pipeline with Gemini, strong guardrails (Pydantic schemas, confidence thresholds), and offline regression tests to prevent drift and hallucination-driven data corruption.
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Key Strengths
- Built production personalization/recommendation API with onboarding, recommendations, and feedback endpoints
- Debugged and fixed inconsistent recommendation results via deterministic routing and graceful partial-result handling
- Reduced latency by adding short-lived caching and pushing filtering constraints upstream (avoid fetch-all-then-filter)
- Designed end-to-end LLM/RAG workflow automation (Gmail job application tracker) and iterated from keyword rules to context-aware pipeline
- Strong agent/workflow architecture: explicit planning vs execution, policy/guardrails, tool allowlists, idempotent writes, structured traces
- Reliability engineering for AI outputs: strict schema validation, confidence thresholds, enums validation, safe fallbacks to heuristics
- Relational data modeling for idempotent ingestion (unique messageId) with constraints/migrations; improved slow queries with composite indexes
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