AI/ML Engineer

Company Description Plurall AI is an AI-powered B2B cybersecurity company specializing in anti-fraud and anti‑deepfake solutions. Powered by the homegrown AI engine, the platform analyzes digital content with near‑perfect accuracy in seconds to help organizations identify and mitigate cyber risks. Plurall AI focuses on protecting businesses from sophisticated cyber threats and fraudulent activities, enabling teams to operate with confidence and stability. By delivering proactive threat detection, the company helps clients avoid unexpected breaches and maintain secure, uninterrupted operations. Role Description This is a full‑time AI/ML Engineer role based in New York, NY with a hybrid work arrangement, allowing a combination of on‑site and work‑from‑home days. The AI/ML Engineer will design, build, and optimize machine learning models and algorithms for cybersecurity use cases, including fraud detection and deepfake identification. Day‑to‑day responsibilities include researching and implementing advanced pattern recognition and neural‑network architectures, evaluating model performance, and iterating on solutions for speed and accuracy. The role involves collaborating with security experts and software engineers to integrate models into production systems, monitoring live performance, and contributing to scalable, secure ML pipelines. The AI/ML Engineer will also document technical approaches, participate in code reviews, and stay current with emerging AI and cybersecurity trends to keep Plurall AI’s solutions at the cutting edge. Qualifications Strong foundation in Computer Science and Algorithms, with experience designing efficient, scalable solutions. Applied expertise in Pattern Recognition and Neural Networks for real‑world AI/ML applications, preferably in cybersecurity or fraud detection. Solid understanding of Statistics and probability for model evaluation, performance measurement, and data‑driven decision making. Proficiency in modern ML tools and languages (e.g., Python, PyTorch or TensorFlow, scikit‑learn) and experience building end‑to‑end ML pipelines. Bachelor’s or Master’s degree in Computer Science, Data Science, Electrical Engineering, or a related quantitative field, or equivalent practical experience. Experience deploying ML models to production environments, optimizing inference performance, and working with cloud platforms (e.g., AWS, GCP, Azure). Familiarity with cybersecurity concepts, anomaly detection, or fraud prevention systems is highly beneficial. Ability to work effectively in a hybrid environment, collaborate across disciplines, and communicate complex technical ideas to non‑technical stakeholders. #J-18808-Ljbffr