A5 Labs offers best-in-breed AI-driven security solutions that ensure fair play and integrity across all strategy-based games, including online poker and beyond. Our proprietary neural networks and deep reinforcement learning models enable non-invasive, high-accuracy detection systems for competitive play. By combining advanced automation detection, exploitative modeling, and AI-driven game security, we help online gaming operators maintain trust, fairness, and integrity at scale. As part of our team, you will be at the forefront of AI security research and development, building cutting-edge solutions that adapt to evolving threats in poker, strategy games, and other competitive gaming environments.
We are seeking a Principal Architect, a hands-on technical manager who will oversee the end-to-end anti-cheat technical pipeline, ensuring scalable, real-time detection. This role will focus on the development, deployment, implementation, and integration of AI models into production environments, working closely with leadership, product, data scientists, ML engineers, and application developers to ensure seamless operation and enforcement of security measures.
Collaborate with leadership to define and lead the technical roadmap for AI-driven anti-cheat systems, ensuring robust, scalable, and real-time fraud detection.
Oversee the entire anti-cheat AI pipeline, from data ingestion and model development to real-time inference and enforcement.
Lead the deployment and integration of neural network & ML models into production applications.
Manage and optimize MLOps workflows, ensuring continuous model retraining, performance monitoring, and operational efficiency.
Collaborate with software engineering teams to integrate AI-driven cheat detection into poker platforms with minimal latency.
Develop and oversee non invasive real-time bot detection models, leveraging contextual fingerprinting, timing analysis, and strategy sequencing
Implement graph-based networks to uncover bot rings and automated decision-making engines
Research and implement adaptive adversarial AI defenses to detect and prevent new forms of cheating.
Lead the development of anti-automation models that identify AI-assisted (RTA) decision-making tools and hybrid human-bot play.
Lead research into game-theoretic AI strategies to detect deviations from normal “human” poker play.
Develop exploitative modeling techniques that analyze betting patterns and decision-making deviations to identify suspicious behavior.
Utilize inverse reinforcement learning and multi-agent simulations to stress-test AI-driven security models.
Manage and mentor a team of ML engineers, researchers, data scientists, and security analysts, fostering a research-driven and operationally effective AI team.
Work closely with product leaders, application developers, security teams, and poker operators to ensure seamless integration of AI-driven anti-cheat solutions.
Define best practices for model monitoring, risk assessment, and real-time fraud alerts.
Collaborate with poker experts, regulatory bodies, and online operators to ensure compliance and effectiveness of AI security measures.
Master’s or PhD in Computer Science, Machine Learning, AI, or a related field.
10+ years of experience in AI, neural networks, and deep reinforcement learning, preferably in gaming, cybersecurity, or fraud detection.
Strong background in AI model deployment and integration, with experience managing end-to-end ML pipelines.
Expertise in MLOps, real-time ML inference, and cloud-based AI deployment (AWS, GCP, or Azure).
Experience working with high-volume, low-latency ML applications that require real-time fraud detection.
Proficiency in Python, TensorFlow/PyTorch, SQL, and distributed computing frameworks (Spark, Kafka, Kubernetes, etc.).
Deep knowledge of game theory, adversarial ML, bot detection, and automation security.
Experience leading technical teams, with a strong balance of hands-on engineering and strategic oversight.
Ability to bridge the gap between research, engineering, and application development, ensuring smooth implementation of AI-driven security solutions.
Strong communication skills to translate complex AI models into deployable, real-world solutions.