Job Description

Job Description
The primary goal is to collaborate with the AI Director, Product Managers, and Engineers to identify and deliver AI-powered security features.
Responsibilities:
Design and implement ML models (e.g. LLM, classifiers, anomaly detectors) in real-time systems such as the WAF and behavioral analysis engine.
Build robust, low-latency ML pipelines using Python, C++, and SQL, ensuring performance, scalability, and maintainability.
Apply adversarial learning and behavioral modeling to differentiate between bots and human traffic in live traffic conditions.
Design and implement workflows that improve data quality, latency, and decision reliability in production services.
Monitor, evaluate and optimize implemented models, ensuring continuous learning and adaptation to emerging threats.
Present results and trade-offs in a clear and structured manner to both technical and non-technical stakeholders.
Contribute to Blackwall's AI engineering practices by mentoring colleagues, conducting code reviews, and improving tools.
Requirements:
3+ years of hands-on experience as an ML/AI engineer working on production systems, ideally in real-time or high-performance environments.
Solid foundations in software engineering practices: version control, CI/CD, containerization, code testing, performance tuning.
Proficient in Python and SQL, with strong exposure to C++ or other systems-level languages.
Deep knowledge of ML models and implementation strategies, especially in classification, anomaly detection, and behavioral modeling.
Experience working with LLM or generative AI frameworks, including fine-tuning and servicing.
Demonstrated ability to translate ML research into scalable and reliable software systems.
Degree in Computer Science, Engineering, Mathematics or a related technical field; master's or doctorate preferred.
Experience working multi-functionally with data, product and infrastructure teams.
Bonus points:
Industry experience in cybersecurity, fraud detection, or bot mitigation.
Knowledge of cloud-native machine learning (ML) tools (e.g. BigQuery, Airflow, Spark) and orchestration tools such as Kubernetes.
Contributions to open source or security-focused machine learning projects.

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