Artificial Intelligence Cybersecurity Engineer

Sev1tech, Inc. Artificial Intelligence Cybersecurity Engineer US-VA-Arlington Job ID: 2025-9252 Type: Full Time W/Benefits Ret Match # of Openings: 1 Category: Engineering Arlington, VA Overview We are seeking a skilled Artificial Intelligence Cybersecurity Engineer to join our team and ensure the seamless deployment, monitoring, and optimization of AI models in production. Sev1Tech is seeking an AI Integration Engineer to integrate AI models into production systems, ensuring robust performance, real-time monitoring, and secure operations. The AI Integration Engineer will bridge the gap between AI model development and production systems, integrating models into applications, APIs, and infrastructure. This role focuses on building dashboards for real-time and historical model health, detecting data drift, and managing AI logging, while ensuring secure‑by‑design practices and alignment with business objectives. Key Responsibilities Model Integration: Integrate AI/ML models into applications (e.g., web, mobile, IoT) using APIs (REST, gRPC) and platforms like TensorFlow Serving or AWS SageMaker. Dashboard Development: Create real‑time and historical dashboards using Grafana, Kibana, or Plotly to monitor model health (e.g., latency, accuracy) and data drift. Drift and Health Monitoring: Implement monitoring pipelines with tools like Evidently AI or Weights & Biases to detect data drift and model degradation, triggering alerts as needed. Logging and Tracing: Set up logging systems with ELK Stack, OpenTelemetry, or LangSmith to capture AI events, errors, and traces for debugging and auditing. Security Implementation: Apply secure‑by‑design principles to protect models and data from vulnerabilities (e.g., adversarial attacks, data leakage) using tools like Adversarial Robustness Toolbox (ART). System Optimization: Optimize model inference for performance (e.g., via quantization, edge deployment) and ensure compatibility with cloud (AWS, Azure) or on‑premises infrastructure. Collaboration: Partner with data scientists to understand model requirements, DevOps for infrastructure alignment, and stakeholders for reporting needs. Testing and Validation: Perform end‑to‑end testing of AI integrations, including stress testing and validation of dashboard metrics. Compliance: Ensure integrations comply with regulations like GDPR, HIPAA, or NIST AI RMF for secure data handling. Responsibilities Education: Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, or a related field. Experience: 4+ years in software engineering or AI integration, with experience deploying AI models in production. Hands‑on experience with dashboarding tools (e.g., Grafana, Kibana) and observability platforms (e.g., Prometheus, Datadog). Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) for AI deployment. Technical Skills: Proficiency in Python; knowledge of JavaScript, C++, or Go is a plus for UI or system‑level integration. Experience with containerization (Docker, Kubernetes) and API development (REST, GraphQL). Expertise in logging frameworks (e.g., ELK Stack, OpenTelemetry) and visualization tools (e.g., Plotly, Chart.js). AI‑Specific Skills: Understanding of AI model metrics (e.g., F1 score, latency) and drift detection techniques (e.g., PSI, KS test). Knowledge of AI vulnerabilities (e.g., prompt injection, model inversion) and mitigation strategies (e.g., differential privacy, ART). Soft Skills: Strong problem‑solving skills for debugging integration issues and optimizing dashboards. Excellent communication to translate technical metrics into business insights. Collaboration skills to work across data science, DevOps, and product teams. Security Clearance: Must be eligible to obtain a Department of Homeland Security EOD clearance (Requirements: 1. US Citizenship, 2. Favorable Background Investigation). Qualifications Experience with LLM‑specific tools like LangSmith or Helicone for monitoring generative AI applications. Familiarity with compliance frameworks (e.g., NIST AI RMF, OWASP AI Security Top 10). Engagement with AI/ML communities, such as X platform discussions on #AISecurity or #MLOps.

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