Point Wild (Formerly Pango Group)

AI Engineer

Point Wild (Formerly Pango Group)

full-time

Posted on:

Origin:  • 🇵🇱 Poland

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Job Level

Mid-LevelSenior

Tech Stack

AWSCloudCyber SecurityPythonPyTorchScikit-LearnTensorflow

About the role

  • Advanced AI Model Development: Designing and optimizing machine learning models (supervised, unsupervised, and NLP-based) to drive security-focused intelligence across the platform.
  • RAG & Embedding Workflows: Building and deploying RAG pipelines, embeddings, and feature stores that enable context-aware, real-time decision-making.
  • Production-Ready AI Systems: Collaborating with Data and MLOps engineers to package, deploy, and monitor models in scalable, cloud-native environments.
  • AI-Driven Analytics: Leveraging deep learning and NLP to provide actionable insights into risk posture, compliance, and security events.
  • Future-Proofing for Quantum: Researching and prototyping AI approaches that can adapt to post-quantum cryptographic standards and automated remediation.
  • Model Development & Optimization: Implement, refine, and maintain ML models (supervised, unsupervised, NLP-based models).
  • Production Integration: Work with Data/MLOps engineers to package, deploy, and monitor models within a robust CI/CD pipeline.
  • Performance Tuning: Optimize model efficacy, efficiency, and reliability.
  • Parallel Project Support: Split or manage multiple AI initiatives to enable faster execution across various R&D efforts.

Requirements

  • At least 4 years of experience in AI/ML Engineering with strong hands-on expertise in developing and deploying production-grade ML models.
  • Hands-on experience with supervised/unsupervised learning, NLP, and deep learning techniques.
  • Proven track record of building, optimizing, and deploying models into production environments.
  • Comfortable working with containerized models, cloud-based AI pipelines (AWS preferred), and CI/CD for ML.
  • Proficiency in Python and ML frameworks (PyTorch, TensorFlow, Scikit-learn, or similar).
  • Performance-driven mindset: skilled in optimizing models for accuracy, efficiency, and scalability.
  • Thrives in a cross-functional team and is eager to learn from and contribute to AI best practices.