AI-driven Automation Engineer

Position: Senior Automation Engineer

Location: NYC, NY (3 days onsite is must)

Duration: 12 Months

Role Summary

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
• Testing discipline — regression validation before auto-merge

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

Continuous Learning

• Tracking emerging agentic/AI remediation tools and evaluating fit before firm-wide adoption