I'm a technical operations specialist focused on reliability, incident response, API troubleshooting, wallet operations, and support automation. My background spans ASIC mining fleets, enterprise security support, Web3 infrastructure, and hands-on projects exploring AI/LLM security workflows.

• Target roles: Junior SRE, Technical Product Support Engineer, Crypto Wallet Ops, Trading Ops, Production Support / Technical Support Engineer
• Current: Ops Manager supporting 2,000+ units across 4+ sites with ~98% uptime and 20+ client stakeholders
• What I do best: I sit at the intersection of infrastructure reliability, onchain operations, and secure automation: monitoring node health, hardening wallet pipelines, analyzing API/security signals, and scripting my way out of manual work.
• Background: Regulated environments (CFTC/NFA) + crypto-native since 2018 (nodes, DeFi, wallet ops)

• Location: USA (currently) | Time zone: US Eastern (ET)
• Engagement: Open to contract (1099) / B2B and full-time
• Remote/Relocation: Open to global remote and relocation to most countries

• Infra/Ops: Linux (Debian/RHEL/Arch), Windows Server, networking (TCP/IP, DNS, DHCP, firewalls), hardware diagnostics, thermal management
• Automation: Bash/Python scripting, PowerShell, ops automation, log analysis, structured alerting
• Monitoring/Observability: Prometheus, Grafana, Docker, systemd, metrics-driven troubleshooting
• Support/Incident: Zendesk / NetSuite / Jira, remote support, incident updates, status reporting, runbooks/SOPs
• API Troubleshooting: Postman, cURL, authentication, headers, JSON payloads, HTTP status codes, webhook testing
• Security (support context): firewall + IDS/IPS concepts, 2FA, secure troubleshooting workflows, API security fundamentals, customer-data handling
• AI/LLM Security: prompt injection basics, OWASP LLM/API risks, RAG/tool-use risk awareness, secure AI-assisted support workflows
• Compliance: documentation controls, audits, regulated-environment discipline

I'm building practical depth in AI/LLM security from an operator's perspective: how AI systems fail in real workflows, how support teams safely use LLMs, and how automation can improve triage without leaking secrets or creating unsafe actions.

Current focus areas:

• LLM application security: prompt injection, insecure output handling, data leakage, tool/plugin abuse, excessive agency
• API security: authentication, authorization, rate limits, webhooks, input validation, OWASP API risks
• Support automation safety: human-in-the-loop workflows, audit logs, escalation boundaries, safe customer-data handling
• Web3 + AI risk: wallet monitoring, transaction alerting, suspicious-address screening, AI-assisted triage for onchain incidents
• Operational AI tooling: using LLMs to summarize incidents, draft runbooks, enrich tickets, and accelerate root-cause analysis

Production-style observability stack for Bitcoin/Monero node uptime, peer connectivity, and infrastructure health.

• Tech: Prometheus, Grafana, Docker, systemd, Linux
• Use case: Real-time visibility into node health, faster detection of connectivity issues, and clearer escalation data for SRE/support workflows

Multi-chain wallet monitoring tool with OFAC screening, structured JSON alert logs, and AI-assisted incident triage potential.

• Tech: Python, PyYAML, Etherscan API, Blockstream API
• Use case: Detect large outflows, flag sanctioned-address interactions, and produce structured alerts that can feed ticketing, SIEM, or LLM-assisted incident summaries

Python/Bash scripts for monitoring ASIC miner health, pool connectivity, and thermal status.

• Tech: Python, Bash, JSON-RPC
• Use case: Standardize daily fleet checks, detect degraded miners faster, and reduce manual diagnosis time at scale

Commerce-focused escrow MVP on Base Sepolia for marketplace and milestone payments.

• Tech: Python, Solidity / smart contract tooling, Base Sepolia, Chainlink CRE
• Use case: Explore payment escrow, dispute events, external resolution workflows, and transparent settlement logic for marketplace transactions
• Status: Work in progress

Hands-on lab for exploring AI/LLM security risks in support and operations workflows.

• Tech: Python, Markdown, JSON, API testing patterns
• Use case: Demonstrate prompt injection, data leakage, unsafe tool-use scenarios, and safer human-in-the-loop support automation patterns
• Status: Planned / in progress

• AI-assisted support workflows for ticket triage, incident summaries, and runbook generation
• LLM security labs focused on prompt injection, data leakage, unsafe tool use, and API abuse patterns
• API troubleshooting workflows using Postman, cURL, structured logs, and webhook testing
• Web3 monitoring tools for wallet activity, node health, and suspicious onchain behavior
• Practical SRE habits: observability, alert quality, escalation clarity, incident documentation, and automation-first operations

• Email: cryptoaijedi@proton.me