Back
Niche ML+cybersecurity skillset reduces applicant density despite metro locations and known employer.
Requires combined ML and cybersecurity expertise, making background fit industry-transferable but domain-specialized.
Explicit 3+ years plus mandatory ML, cloud, MLOps, and security tooling implies high filtering strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and implement scalable AI infrastructure for cybersecurity including training, deployment, and monitoring of AI models.
Build and optimize AI-driven tools for threat detection, triage, automated response, and integrate with existing security platforms like SIEM, SOAR, and EDR.
Lead development of data pipelines and feature engineering workflows for security telemetry; ensure AI system reliability, performance, and security in production.
Bachelor’s or Master’s degree in Computer Science, AI, or related field.
3+ years of experience building and deploying machine learning or AI systems in production.
Strong programming skills in Python; experience with ML frameworks (TensorFlow, PyTorch).
Experience with cloud-based AI infrastructure (AWS, Azure, or GCP), containerization (Docker, Kubernetes), and knowledge of cybersecurity tools (SIEM, IDS/IPS).
Experienced in applying AI and ML to cybersecurity use cases, including automated threat detection and response workflows.
Proficient in developing and deploying AI systems with MLOps best practices in cloud environments.
Capable of mentoring junior engineers and collaborating with cybersecurity teams to translate needs into AI solutions.