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3 Weeks ago

Senior Machine Learning Engineer, Anti-Cheat Ai- Security (Remote)

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Bangalore, Karnataka, India
Information Technology
Other
A5 Labs

Overview

Key Responsibilities

Machine Learning Pipeline Development & Deployment

  • Design and deploy scalable ML/DL pipelines for real-time fraud and bot detection in high-volume competitive gaming networks.

  • Optimize deep learning models for low-latency inference and real-time decision-making.
  • Automate model training, tuning, and deployment using MLOps best practices.
  • Implement distributed computing techniques to process large-scale poker data efficiently.

Automation & Bot Detection

  • Develop and deploy real-time bot detection models, leveraging behavioral biometrics, timing patterns, and clickstream analysis.

  • Implement graph-based analytics to uncover multi-accounting automation, bot rings, and coordinated fraud.
  • Optimize AI-driven countermeasures to detect hybrid human-bot play and adversarial AI threats.

Game Theory & Exploitative Modeling

  • Support the integration of game-theoretic AI models into real-time detection pipelines.

  • Develop exploitative modeling features to detect unnatural betting patterns.
  • Implement multi-agent simulations to test and validate anti-cheat AI strategies.

MLOps & Engineering Best Practices

  • Design and implement robust CI/CD pipelines for ML models in anti-cheat applications.

  • Ensure high availability and fault tolerance for fraud detection systems.
  • Optimize inference models for low-latency execution in production environments.
  • Work with cloud platforms (AWS, GCP, or Azure) to deploy and scale AI security models.
  • Monitor and log model performance, ensuring continuous improvement and retraining.

Cross-Functional Collaboration

  • Work closely with data scientists, software engineers, and poker security experts to align ML solutions with business needs.

  • Collaborate with game developers to integrate anti-cheat AI into poker platforms.
  • Partner with poker analysts to fine-tune model accuracy and identify new threats.


Technical & Experience Requirements

Technical Skills

  • Master’s or PhD in Computer Science, Machine Learning, AI, or a related field.

  • 5+ years of experience in ML/DL model development, optimization, and deployment.
  • Strong programming skills in Python, SQL, and distributed computing frameworks (Spark, Kafka, Kubernetes, etc.).
  • Expertise in TensorFlow, PyTorch, or Scikit-learn for ML model development.
  • Experience with real-time ML inference, feature engineering, and data pipelines.
  • Hands-on experience with MLOps, cloud deployment (AWS, GCP, Azure), and Kubernetes/Docker.
  • Familiarity with fraud detection techniques, adversarial AI, and anomaly detection.
  • Experience working with large-scale structured and unstructured data for fraud modeling.
  • Understanding of graph-based fraud detection and multi-agent AI techniques.

Preferred Experience

  • Preferred experience working with real-time fraud detection systems in gaming, cybersecurity, or fintech.

  • Knowledge of game theory, Nash equilibrium, and exploitative modeling.
  • Familiarity with multi-accounting fraud detection and adversarial ML.

Experience in reinforcement learning, inverse reinforcement learning, or multi-agent systems.
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