Tech Stack
AWSCloudCyber SecurityPythonPyTorchScikit-LearnTensorflow
About the role
- Design, implement, and optimize machine learning models (supervised, unsupervised, NLP-based) to drive security-focused intelligence.
- Build and deploy RAG pipelines, embeddings, and feature stores for context-aware, real-time decision-making.
- Collaborate with Data and MLOps engineers to package, deploy, and monitor models in scalable, cloud-native environments (CI/CD).
- Leverage deep learning and NLP to provide actionable insights into risk posture, compliance, and security events.
- Research and prototype AI approaches that can adapt to post-quantum cryptographic standards and automated remediation.
- Implement, refine, and maintain ML models, perform performance tuning, and support multiple parallel AI initiatives.
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.
- Experience with containerized models, cloud-based AI pipelines (AWS preferred), and CI/CD for ML.
- Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, Scikit-learn, or similar tools.
- Skilled in optimizing models for accuracy, efficiency, and scalability (performance tuning).
- Ability to collaborate in cross-functional teams and contribute to AI best practices.