Cognyte Senior Machine Learning Engineer 

Bulgaria, Sofia 

14155967

Today’s world is crime-riddled. Criminals are everywhere, invisible, virtual and sophisticated. Traditional ways to prevent and investigate crime and terror are no longer enough…

Technology is changing incredibly fast. The criminals know it, and they are taking advantage. We know it too.

For 30 years, the incredible minds at

Our product:

Our analytics platform powers our portfolio. Data is growing rapidly and is highly fragmented across different departments, and organizations are looking to adopt open software to keep up with the pace of technology. These market shifts require security organizations to deploy an analytics platform that can connect different solutions from eitheror third parties to effectively develop new capabilities.

As a Cognyter you will:

• Participate in High Level Research and Design of Machine Learning Models in Computer Vision / NLP to meet product requirements
• Productionize ML models into containerized microservices
• Get our ML models working in a generic way on GKE (Google Kubernetes Engine) / and on-prem Cloud Native – for integration with our dev teams
• Extract, transform and load datasets into formats suitable as features for model training
• Integrate the system with various Machine Learning models for purposes of training / inference
• Deploy of GPU accelerated pods on Kubernetes on GKE / on-prem – for integration with our dev teams
• Implement an end-to-end Machine Learning pipeline infrastructure

For that mission you’ll need:

• Experience or Final project/Courses in Machine Learning or Deep learning
• Programming language: python
• Notebooks: jupyter / colab
• Data munging: pandas, numpy, matplotlib
• Microservices: django
• Infra: git, bash, cloud, docker
• Machine learning frameworks: Scikit Learn
• Deep learning frameworks: TensorFlow, PyTorch

Nice to have:

• Kubernetes, Bash, Google Cloud
• MLOps: KubeFlow, TFX, TensorBoard
• Computer Vision and natural language processing
• Model architectures: Convnets, LSTM RNNs, Graph Neural nets, Variational Encoders, GANs, Wide Context Transformers