Senior Security Operations Engineer

Required Qualifications

• Required: Public Trust Clearance – Candidates must be able to obtain and maintain a US public trust clearance.
• Bachelor’s degree in Computer Science, Cybersecurity, or equivalent professional experience.
• Solid experience in cloud environments (AWS, GCP, or Azure), with strong understanding of cloud-native threats.
• Proficiency in scripting languages (e.g., Python, Bash) for automation and tooling development.
• Hands-on experience with SOC tools and platforms, such as SIEM (Splunk, Sentinel, etc.), SOAR, EDR/XDR, and log management.
• Strong understanding of incident containment and eradication strategies, with proven ability to coordinate response with technical teams.
• Familiarity with security frameworks and standards (NIST 800-61, CIS Controls, MITRE ATT&CK, ISO 27001).
• Excellent analytical, critical thinking, and problem-solving skills.
• Ability to consume and synthesize intelligence about actors, techniques or situations to identify emerging risk scenarios.
• Proficiency in process formulation and improvement.
• Background in threat modeling, adversary emulation, and risk-based alert tuning.
• Strong communicator with the ability to explain security risks and actions to both technical and non-technical audiences.
• Proven track record of leading cross-functional efforts in high-pressure situations.
• Ability to foster collaboration across InfoSec, IT, and engineering teams.
• Forensics experience, investigating incidents and preserving digital evidence.
• Leverage AI to automate and optimize security operations workflows, including alert triage, enrichment, and incident classification.
• Design and maintain AI-assisted runbooks, ensuring consistency, auditability, and human-in-the-loop validation for critical decisions.
• Identify opportunities to improve SOC efficiency through AI-driven automation, while maintaining strong controls and avoiding over-reliance on unverified outputs.
• Integrate security tooling with AI platforms and APIs to streamline investigation, response, and reporting processes.
• Enhance vulnerability management and incident response workflows through intelligent prioritization, correlation, and contextualization of findings.
• Continuously evaluate the accuracy, reliability, and security implications of AI-assisted decisions in operational environments.