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Senior AI Automation Engineer

IMR Soft LLCNew York, NY🇺🇸United StatesPosted Sep 30, 2026

Quick Overview

Seniority
Mid Senior
Work mode
On Site
Location
New York, NY, United States
Posted
19 hours ago
DockerSQLAirflowBashGitHub ActionsJenkinsLLMPython

Job Description

Position: Senior AI Automation Engineer
Location: NYC, NY (3 days onsite is must)
Duration: 12 Months
Role: Automation Engineer — AI-Driven Code Security & Remediation Framework
focus to primary = 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
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
Skill Levels
  • Expert Level resource: 8 to 10 or more total experience out of which at least three years of experience in the relevant AI driven automation for code scanning and remediation matching the above skills
  • Advanced level: Total 6 – 8 years of total experience out of which at least three years of experience in the relevant AI driven automation for code scanning and remediation matching the above skills

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