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Mike Fernandez
Mid-Level Software Engineer specializing in cloud data platforms and CI/CD
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About
AI/LLM engineer who has owned end-to-end production delivery of multi-agent RAG systems on Azure (React + FastAPI + data pipelines + Terraform), including rigorous evaluation/monitoring and reliability guardrails. Shipped an AI-driven observability root-cause analysis assistant that reduced MTTR ~30%, cut alert noise ~20%, and reached ~70% adoption in the first month; also built a clinical document Q&A system with citations and compliance-oriented controls.
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Key Strengths
- Owned and shipped end-to-end AI feature across frontend, backend, data pipelines, and Azure infrastructure
- Productionized multi-agent LLM/RAG systems with structured outputs and schema enforcement for reliability
- Built rigorous LLM testing/evaluation (replay framework, unit tests with mocked LLMs, top-k precision tracking)
- Strong production monitoring/observability for agentic systems (latency, token usage, failure tracing, alerting on low confidence)
- Delivered measurable ops impact: ~30% MTTR reduction, ~20% noise reduction, ~70% adoption within first month
- Legacy modernization with low-regression migration strategy (APIs, feature flags, parallel runs, traffic mirroring, contract tests, historical replay)
- Effective stakeholder alignment with CTO-level ambiguity into a focused roadmap and rapid iteration (2–3 week thin-slice delivery)
- Uses AI-assisted dev tools for architecture/debugging/refactoring (identified race conditions, redesigned to explicit state machine, modularized agents)
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