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Executive Summary

I design and ship autonomous, self-optimizing software systems—from strategy to production. My work reduces operational drag, cuts manual toil, and increases reliability using agentic architectures, rigorous evaluation loops, and observability-by-design.

  • Outcome-first: measurable time-to-autonomy, SLO adherence, and failure recovery benchmarks.
  • Agentic-by-default: multi-agent coordination with guardrails, audits, and closed-loop learning.
  • Production-grade: zero-downtime rollouts, progressive delivery, and resilient data flows.

Focus Areas

Area What I Deliver
AI Automation Multi-agent orchestration, policy/guardrail layers, automatic evaluation (quality, safety, drift)
Back-End Systems Event-driven services (Go/Node), low-latency APIs, streaming, queues, idempotent pipelines
Observability Metric/trace-first design, anomaly detection, SLO dashboards, self-heal hooks
Platform Ops Docker, Kubernetes, GitOps, progressive rollout, secure Secrets & config
Data Layer Postgres, Redis, Vector DBs, schema/version control, durability & backup strategies

Operating Principles

  • Design for autonomy: every workflow has owner agents + watcher agents + evaluator agents.
  • Bias for reliability: golden paths, circuit breakers, graceful degradation.
  • Evidence over opinion: decisions flow from telemetry, not vibes.
  • Tight feedback loops: small changes, measurable impact, fast rollback.

Architecture Playbook (Highlights)

  • ⚙️ Orchestration: event buses, fan-out/fan-in, CRON + on-demand triggers, human-in-the-loop only when needed.
  • 🧠 Agents: role-specialized, tool-aware, with persistent memory + retrieval, audit trails for every action.
  • 🛡 Safety & Quality: policy checks, sandboxing, offline simulation runs, A/B validation against ground-truth.
  • 📈 Observability: SLIs/SLOs defined at inception, auto-ticket on breach, self-heal runbooks attached to alerts.

Live Metrics

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Engagement Model

  • Define outcomes & SLOs: success metrics upfront (latency, error budgets, automation coverage).
  • Architect & simulate: model agents + workflows, run failure simulations and load tests.
  • Deliver & measure: ship with progressive delivery and observable impact.
  • Iterate toward autonomy: expand agent responsibilities, shrink human touch points.

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