Works/Profile/professional_profile

Professional Profile

Who I am, professionally

Role: Lead Software Developer/Engineer, targeting Principal Engineer
Domain (current job): Technology and Operations — specifically Network (Network Automation, Network Observability, Network Validation, Network Certification, Patches, Power Maintenance)
Cross-cutting work: AI features, chatbots, MCPs (CLI, Toolbox, Grafana), Software-Defined Networking, cloud performance, infra performance engineering, monitoring, anomaly detection, traffic observability/automation, security & vulnerability patch management

Career framing — not domain-locked

I think of myself like a contractor — I go wherever the job is, not fixed to Network long-term. I'm actively targeting Fintech, Capital Markets, BigTech, Healthcare, E-commerce, and Entertainment — domains that employ across every niche rather than one. My role trajectory beyond Lead/Principal Engineer also includes Platform Engineering and Solutions/Solution Architect tracks.

When I (or anyone advising me, including Claude) frame learning content, career advice, or "why this matters" reasoning, the default should be broad senior/staff/principal growth — system design maturity, tradeoff articulation, architecture communication, platform-thinking — tied back to current Network tooling only when it naturally fits, never as the forced default. See tech_stack for how this same lens applies to tool choices.

Projects my team already runs (production, where I have also contributed)

Project	What it does
PIV	Correlates ServiceNow change requests → automated pre-check/post-check across 1000s of devices, 50+ commands each → AI analysis for pass/fail/warning → archived with reviews back to ServiceNow; also supports ping on all hosts or IPs
Network Certification	Ansible playbooks run post-PIV-implementation → test results processed → network team manually marks image "golden" — zero AI hallucination tolerance here
Aegis	App-to-app dependency mapping — which apps talk to which, live traffic between them
NetOps	Network Chatbot — users can run network queries/commands in natural language, e.g. give me packets from last 5 minutes of source x, what is health of app code Y, give all fabric faults report, show me all fabrics and their network details

Tools & technologies — depth vs. breadth

Across different job roles I've touched a wide range of tools/technologies/practices; the last ~2 years specifically have concentrated my depth in the Network domain. Outside that vertical, most of the rest is genuine hands-on from prior roles or self-directed work — not just reading about it — with a track record of ramping to working fundamentals fast on anything new. When framing me for a role, both tiers below are fair game — the second tier is "can be productive in week one," not "unfamiliar."

Production depth (current role, ~2 years, daily use): ServiceNow, NetBrain, ExtraHop, Corvil (recent ~$10M investment), Grafana, Elasticsearch (soon to be end-of-life support — looking for opensource, manageable replacement), MyOps (network inventory DB), SQL Server Data Warehouse (inventory), Infoblox, Cisco APIC, Cisco Arista, Cat9k, NetBox, Elastiflow

Broader platform toolkit (prior roles / self-directed — full hands-on or fast-ramp fundamentals):

Cloud (AWS): EC2, ECS, EKS, Lambda, S3, RDS, Redshift, Athena, CLI, Well-Architected Tool, Route 53
CI/CD, hosting & DevSecOps: GitHub, GitHub Actions, GitHub Pages, Cloudflare, Cloudflare Pages, JFrog Artifactory, SonarQube (code quality), Snyk (SCA/SAST), Aqua Security (container image scan), Bright Security (DAST), Terraform, Ansible, Docker, Kubernetes, Helm
Databases: PostgreSQL, MySQL, SQLite, MongoDB, Redis
Data orchestration: Airflow, Argo Workflows
AI/agent engineering: LangChain, LangGraph, Amazon Bedrock AgentCore, Claude/Claude Code, Windsurf, MCP
Languages & frontend: Go, Python, TypeScript, React, Next.js, Tailwind CSS
Network automation (adjacent to daily-use tools above): Slurp'it, NetPicker

AI integration across the above: this isn't a siloed "AI tools" bullet — agentic/AI tooling (LangChain, LangGraph, Bedrock AgentCore, MCP) has been applied into the other categories: AI-assisted layers on top of CI/CD (automated review/triage), observability (anomaly detection on Grafana/Elasticsearch-class stacks), and data pipelines (Airflow-orchestrated enrichment) — same "AI for measurable business value, not just personal tool use" framing as tech_stack.

Full catalog with notes on each: Tools.

Teams I interact with