AI Solutions Engineer - Machine Learning Models

Responsibilities :

- Design, develop, and implement robust data pipelines for collecting, cleaning, and preparing data for model training and evaluation, specifically from web and API traffic, and security event logs.

- Select appropriate machine learning models, with a particular emphasis on smaller, efficient models suitable for security applications (e., WAF, bot detection, anomaly detection, API threat prevention) and other performance-critical use cases.

- Train, fine-tune, and evaluate machine learning models, employing techniques to optimize for performance, cost, and accuracy in identifying and mitigating security threats.

- Deploy models into production environments, establishing and managing MLOps processes for continuous integration, delivery, and training (CI/CD/CT) within our cloud security infrastructure.

- Monitor model performance in production, implementing strategies for regular re-tuning and updates to ensure continued accuracy and relevance against evolving threat landscapes.

- Collaborate with product management and engineering teams to understand requirements, define AI solutions, and integrate them into existing products and new features for web and API security.

- Drive the evolution of our MLOps practices to enhance the speed, reliability, and scalability of our AI deployments, fostering a culture of continuous improvement and innovation in AI operations.

- Stay up-to-date with the latest advancements in applied AI, MLOps, and relevant technologies, particularly in cybersecurity AI, threat intelligence, and Generative AI for security.

- Document AI solutions, processes, and model performance for internal stakeholders.

Candidate Profile :

- 4+ years of hands-on experience in applied AI or machine learning engineering, preferably in a cybersecurity context.

- Proven experience in developing, deploying, and maintaining machine learning models in production environments for security use cases.

- Strong proficiency in Python and relevant AI/ML libraries/frameworks (e., Scikit-learn, TensorFlow Lite, PyTorch, ONNX, Hugging Face Transformers, MLflow, Kubeflow).

- Hands-on experience with data cleaning, feature engineering, model selection, and hyperparameter tuning, particularly for smaller, efficient models tailored to security data.

- Demonstrable experience in building and maintaining robust data pipelines and CI/CD/CT for ML systems.

- Software development experience in building high-performant, secure, and scalable web applications or security services.

- Fair understanding of dynamically scalable cloud architectures, ideally AWS.

- Excellent problem-solving and analytical skills.

- Strong verbal and written communication skills.

- Collaborative, quality-conscious, and self-motivated with a proactive approach.

- A passion for building, deploying, and meticulously managing the full lifecycle of impactful AI systems.

- Experience with security AI use cases like anomaly detection, threat intelligence, or user behaviour analytics.

- Experience with Layer 7 security concepts, web application firewalls (WAF), API security, and bot mitigation techniques.