AI Software Engineer

• The Agentic AI Software Engineer – Cybersecurity Systems designs, develops, and deploys advanced AI-driven software solutions to enhance cybersecurity detection, response, analysis, and automation capabilities
• This role focuses on building and maintaining agent-based artificial intelligence systems capable of autonomously generating code, conducting security analyses, triaging alerts, identifying vulnerabilities, and recommending remediation strategies, with structured human oversight and validation
• The position requires the application of advanced knowledge in computer science, artificial intelligence, machine learning, secure software engineering, distributed systems, and cybersecurity principles
• The role involves complex system design, secure AI integration, and the development of enterprise-grade security automation platforms
• Architect and implement AI-powered systems that perform autonomous or semi-autonomous cybersecurity tasks, including vulnerability analysis, threat detection, alert triage, log analysis, and secure code review
• Design multi-agent architectures capable of orchestrating specialized AI agents (e.g., vulnerability scanning agents, log analysis agents, exploit pattern detection agents)
• Develop retrieval-augmented generation (RAG) pipelines to enable AI systems to securely query internal knowledge bases, threat intelligence feeds, CVE databases, and security documentation
• Utilize large language models (LLMs) to generate secure software components, scripts, detection logic, and test cases
• Review and refine AI-generated code to ensure compliance with secure coding standards (e.g., OWASP Top 10, secure SDLC practices)
• Implement automated guardrails to detect insecure outputs, prompt injection vulnerabilities, model hallucinations, and data leakage risks
• Design validation frameworks to benchmark AI-generated security outputs against known vulnerability patterns
• Design and implement reinforcement learning from human feedback (RLHF) workflows for cybersecurity use cases
• Develop evaluation metrics for AI accuracy in threat detection, vulnerability identification, and remediation recommendations
• Continuously refine prompt engineering strategies, model tuning parameters, and system architecture to improve security reliability and precision
• Implement explainability mechanisms to support auditability and defensibility of AI-driven security decisions
• Develop and maintain APIs and microservices to integrate AI-powered security tools into enterprise platforms
• Architect scalable and fault-tolerant distributed systems to support real-time security event processing
• Deploy AI-enabled services within secure cloud environments (e.g., AWS, Azure, GCP) using containerization and orchestration technologies (Docker, Kubernetes)
• Integrate AI systems with SIEM, SOAR, vulnerability management, and endpoint detection platforms
• Conduct threat modeling for AI systems to identify adversarial risks, model exploitation vectors, and prompt injection vulnerabilities
• Implement secure authentication, authorization, encryption, and data governance controls
• Develop safeguards to prevent misuse of AI systems in exploit development or unintended security bypass scenarios
• Perform security reviews and penetration testing of AI-driven software components
• Design automated test pipelines for AI-driven security workflows
• Conduct adversarial testing to evaluate resilience against malicious inputs
• Monitor system performance metrics (latency, false positive rates, detection accuracy)
• Optimize inference pipelines and distributed systems for reliability and scalability
• Produce detailed architectural documentation for AI security systems
• Collaborate with cybersecurity researchers, product teams, software engineers, and cloud architects to translate threat intelligence and security requirements into scalable AI-enabled solutions
• Provide technical guidance on AI governance, responsible AI deployment, and secure AI lifecycle management

Benefits

• Full medical, dental, and vision
• Paid vacations and holidays
• Phone plan
• Commuter benefits program
• Maternity and paternity leave
• Training budgets. Cool swag. Stocked fridges
• Remote-first

• Bachelor’s degree or higher in Computer Science, Computer Engineering, Software Engineering, Artificial Intelligence, Cybersecurity, Information Security, or a closely related technical field
• Experience implementing secure software development lifecycle (SSDLC) practices
• Experience working with large language models (LLMs) and prompt engineering techniques
• Experience with AI orchestration frameworks (e.g., LangChain, LlamaIndex, AutoGen, Semantic Kernel, or similar)
• Familiarity with containerization and orchestration tools (Docker, Kubernetes)
• Strong foundation in algorithms, data structures, and object-oriented programming
• Proficiency in programming languages such as Python, Java, C++, or similar
• Knowledge of cybersecurity concepts including vulnerability management, threat modeling, penetration testing, SIEM/SOAR platforms, and common attack vectors
• Experience deploying applications in cloud environments (AWS, Azure, or GCP)
• Experience building RESTful APIs and microservices architectures
• Experience developing AI or machine learning systems for security-related applications
• Master’s degree in Computer Science, Artificial Intelligence, or Cybersecurity
• Experience designing AI systems for automated threat detection or exploit analysis
• Experience integrating AI systems with cybersecurity tooling ecosystems
• Experience with reinforcement learning, fine-tuning, or model evaluation frameworks
• Familiarity with NIST, ISO 27001, or similar security standards