Senior Cybersecurity Engineer I

Responsibilities

• We are seeking an AI/ML Security Data Scientist to develop, evaluate, govern, and operationalize machine-learning security controls that protect enterprise generative-AI applications and traditional machine-learning systems.
• This position will own critical capabilities involving prompt-injection detection, LLM vulnerability assessment, audio redaction, adversarial-model testing, and secure-model-development practices. The successful candidate will combine strong applied data-science skills with an understanding of cybersecurity, model-risk governance, production ML operations, and regulatory documentation.
• The role will work closely with AI Firewall engineering, AI Security Enablement, Model Risk Management, Technology Risk and Information Security, product owners, auditors, and external technology providers.
• AI security model development and evaluation:
• Develop, evaluate, and improve machine-learning controls for prompt-injection detection, LLM vulnerability detection, sensitive-data redaction, and related AI-security use cases. Benchmark open-source, internally developed, and third-party models using representative test datasets. Design training, validation, out-of-sample, and out-of-time evaluation datasets using sound sampling, labeling, and data-quality practices. Evaluate model performance using recall, precision, F1 score, Matthews correlation coefficient, accuracy, ROC-AUC, confusion matrices, and other appropriate measures.
• Recommend operating thresholds that balance detection effectiveness, false positives, operational impact, and business risk.
• Evaluate emerging audio, image, and video deepfake-detection technologies where relevant to the AI-security portfolio.
• Model-risk management and governance:
• Own and maintain model documentation for BERT-based prompt-injection detection, LLM vulnerability assessment, and other assigned AI-security models. Prepare model-validation submissions and coordinate responses to Model Risk Management Group requests. Document model purpose, methodology, datasets, assumptions, limitations, compensating controls, performance results, implementation testing, and ongoing monitoring.
• Define class-level performance monitoring and feature-, concept-, and probability-distribution-drift controls. Establish escalation, remediation, retraining, fallback, and model-deactivation criteria. Ensure documentation is internally consistent, technically defensible, traceable to evidence, and ready for independent review or audit. Audio redaction and sensitive-data protection
• Support the design and implementation of audio-redaction capabilities for enterprise AI applications. Assess centralized-service and embedded-SDK deployment approaches, including scalability, auditability, separation of duties, maintainability, language independence, and circumvention risk.
• Develop evaluation datasets and acceptance criteria for detecting and redacting sensitive data in voice and audio interactions. Partner with platform and application teams to integrate audio-redaction capabilities into production workflows. Secure model development lifecycle
• Conduct adversarial stress testing and robustness assessments for NLP, large-language, computer-vision, and tabular models. Identify model weaknesses, failure modes, integrity issues, and adversarial examples, and recommend remediation.
• Support model scanning, secure model hosting, protection from unsafe model artifacts, and controls addressing model-serialization and software-supply-chain risks. Help integrate model-security testing into APIs, CI/CD pipelines, container platforms, and application-development processes.
• Contribute reusable tools and processes that reduce the time required to securely evaluate and deploy models. Production enablement and operational support
• Partner with AI Firewall engineers to integrate approved models into production security controls. Support the rollout and monitoring of BERT-based prompt-injection detection across generative-AI and agentic use cases.
• Support upgrades or replacements for existing LLM vulnerability-assessment capabilities and the controlled retirement of legacy models.
• Investigate performance degradation, drift, unexpected model behavior, and production detection issues. Maintain reproducible code, datasets, model artifacts, test results, technical decisions, and transition documentation.
• Stakeholder engagement:
• Translate technical model results into clear risk conclusions and recommendations for technical and nontechnical stakeholders.
• Work with model owners, product teams, engineers, cybersecurity teams, governance functions, auditors, and vendors.
• Present findings, design decisions, limitations, and remediation plans to review committees and senior stakeholders.
• Provide evidence, demonstrations, and technical explanations for regulatory, risk, and audit reviews.

Qualifications

• Minimum Qualifications:
• Bachelor’s degree in computer science, data science, statistics, mathematics, cybersecurity, engineering, or a related field, or equivalent practical experience. Five or more years of experience in data science, machine-learning engineering, cybersecurity analytics, or applied AI.
• Practical experience developing and evaluating classification or detection models. Strong Python skills and experience with common data-science and ML libraries such as pandas, scikit-learn, PyTorch, TensorFlow, or Hugging Face Transformers.
• Strong understanding of:
• Training, validation, holdout, out-of-sample, and out-of-time testing,
• Sampling, labeling, data quality, class imbalance, and statistical evaluation,
• Classification metrics, model thresholds, drift detection, and ongoing monitoring,
• Model limitations, compensating controls, and production fallback strategies,
• Experience deploying or integrating ML models through APIs, containers, Kubernetes, or CI/CD pipelines. Ability to produce rigorous technical and governance documentation.
• Strong written and verbal communication skills, including the ability to respond to independent model reviewers and auditors.

About Company

Company