Builds and operates a system that scans GitHub/Artifactory repos for vulnerabilities and EOL libraries, then uses AI-driven automation to remediate findings — cutting manual triage and patch time firm-wide.
Core Technical Skills
- Programming: Strong Python (scanners, orchestration, API integration); basic Bash for CI/CD glue
- Source & Artifact Systems: GitHub (Actions, Advanced Security, PR workflows) and JFrog Artifactory/Xray; ability to scale across multi-repo, multi-language codebases
- Scanning Tools: Hands-on with Snyk, Xray, CodeQL / Dependabot, Trivy, or Semgrep; understanding of CVE/CVSS scoring and EOL-detection sources (e.g., endoflife.date)
- AI-Driven Remediation: Building agentic workflows (LLM-based) that interpret findings, generate patch PRs, run tests, and summarize fixes; prompt engineering for code-editing agents
- CI/CD & Orchestration: Integrating scan-and-fix pipelines into GitHub Actions/Jenkins; Docker for isolated fix-testing; scheduling via Airflow/cron
- Reporting: Structuring findings (JSON/SQL) into dashboards for tracking coverage and trends
Supporting Skills
- Security fundamentals (injection, auth flaws, supply-chain/SBOM risk)
- Risk-based prioritization beyond raw CVSS scores
- Semantic versioning awareness for safe auto-upgrades
Communication Skills
- Translating vulnerability data into concise, risk-framed leadership updates (exposure counts, MTTR, fix-rate trends)
- Writing clear status emails on scan coverage and outstanding critical items
- Building the business case (time saved, risk reduced) for non-technical stakeholders
- Tracking emerging agentic/AI remediation tools and evaluating fit before firm-wide adoption