We are seeking a Senior Security Engineer (Incident Response & Threat Hunting) to join our Cyber Defense team.
This role focuses on proactive threat hunting, incident response, security automation, and Detection-as-Code (DaC) across complex hybrid environments spanning AWS, Azure, on-premises infrastructure, and bare-metal systems. You will work closely with our AI Engineering team to advance intelligent, automation-driven security operations while supporting the investigation, containment, and remediation of security incidents.
Core Responsibilities
1. Advanced Incident Response & Active Remediation
- End-to-End IR & Forensics: Lead incident response operations across diverse architectures: Multi-Cloud, on-premise infrastructure, and physical servers. Perform advanced memory forensics, disk imaging, and root-cause analysis.
- Active Containment & Customer Support: Act as the highest technical escalation point to assist customers during active breaches. Execute rapid remediation strategies by isolating compromised hosts via EDR/XDR, deploying custom WAF rules, and implementing zero-trust Firewall blocks.
- Automated Forensics: Architect and deploy automated forensic data collection pipelines in cloud environments and manage incident case lifecycles utilizing platforms like DFIR-IRIS.
2. Threat Hunting, DaC & Platform Engineering
- Proactive Threat Hunting: Execute hypothesis-driven campaigns across petabyte-scale data lakes to uncover advanced persistent threats (APTs) that bypass traditional SIEM solutions.
- Detection Engineering: Translate traditional, static correlation rules into Detection-as-Code (DaC). Write, test, and deploy detections using version control (Git) and CI/CD pipelines.
- Streaming Analytics: Build and optimize high-throughput, stateful detection logic using Apache Flink and modern data routing pipelines.
3. AI-Driven Security & Automation (LLM Integration)
- Automation Development: Write robust scripts and microservices (Python, Go, or Bash) to automate repetitive IR tasks, containment actions, and evidence gathering.
- AI Agent Workflows: Design playbooks, prompt scenarios, and logic flows for AI security assistants (Agents) to accelerate log analysis and automate initial forensic triage.
- Local LLM Training: Collaborate directly with AI Engineers to curate high-quality \"Data Kits\" (sanitized logs, attack payloads, incident timelines). Use these datasets to fine-tune and train our proprietary, air-gapped local LLMs to autonomously detect, triage, and execute Level 1/Level 2 incident response.
Basic Qualifications
- Experience: 4+ years in Incident Response, Threat Hunting, or Security Engineering within complex enterprise or MSSP environments.
- Enterprise SIEM & SOC Platforms: Deep operational knowledge of industry-leading solutions such as Splunk, IBM QRadar, ELK Stack, Google SecOps, or Microsoft Sentinel.
- Modern Data Stack (Alethea Platform ecosystem): Hands-on experience or strong familiarity with high-performance logging, streaming, and forensic architectures including OpenSearch, ClickHouse, Apache Flink, Vector, and DFIR-IRIS.
- Security Stack Expertise: Proven ability to execute incident containment through major EDR/XDR solutions (e.g., CrowdStrike, SentinelOne), WAFs (e.g., Cloudflare, F5), and Next-Gen Firewalls (e.g., Palo Alto, Fortinet).
- Engineering Mindset: Strong programming and scripting skills (Python is mandatory; Go, Rust, or Bash is a plus) with a proven track record of automating security workflows.
- Hybrid Architecture: Deep technical understanding of Cloud architecture (AWS/Azure/GCP) and traditional on-premise/physical server networking (TCP/IP, Active Directory, virtualization).
Preferred Qualifications
- Certifications: Highly regarded industry certifications such as GIAC GCIH, GCFA, GNFA, or offensive certifications (OSCP).
- AI/ML Exposure: Familiarity with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) concepts, or experience building AI prompts for cybersecurity use cases.
- DevSecOps: Experience with CI/CD pipelines (GitLab, GitHub Actions), containerization (Docker, Kubernetes), and Infrastructure as Code (Terraform).