Role Summary

We are seeking an experienced and hands-on AI Solutions Engineer to join our dynamic team. In this role, you will be responsible for the end-to-end lifecycle of applied AI solutions, focusing on security and innovative Generative AI use cases directly relevant to protecting web and API assets. You will collaborate closely with Product Management and other engineering teams to design, develop, tune, deploy, and maintain cost-effective and performance-efficient machine learning models. While your primary focus will be on selecting, training, and optimizing models, you will also play a key role in guiding development teams on integrating these AI solutions seamlessly into our products. This is a practical, implementation-focused role centered on the ongoing lifecycle and operationalization of AI, leveraging MLOps best practices to bring cutting-edge AI and Generative AI solutions to life.

Key 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.g., 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.

Required Qualifications & Skills

• 3-5 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.g., 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.

Preferred Qualifications

• 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.