Senior Machine Learning Engineer

Key Responsibilities:

• Work with the existing AI Operations (AIOps) team to maintain and enhance an in-house developed Cyber Security Machine Learning Platform for the AI Team and Deployment Team
• Develop integrated services to automate the retraining and deployment of Machine Learning and LLM-based models
• Deploy and maintain Large Language Models (LLMs) in production using production-ready serving frameworks such as vLLM, TensorRT, and similar
• Ensure that deployed machine learning and LLM models remain functional and performant in production
• Develop end-to-end pipelines as part of AIOps for developed models
• Ensure that AI/ML applications are scalable on Kubernetes (on-premise and cloud)
• Work on optimisation so that models can handle large volumes of data in a production environment
• Embody the company's core values in work and interactions with colleagues, customers and vendors

Qualifications/Requirements

• Ability to do development using Python and C++
• Ability to design and implement on-prem and cloud solutions (AWS, Azure or GCP)
• Ability to build AIOps pipelines using CI/CD tools such as GitLab, AWS Code Pipeline, etc
• Good understanding of Linux and the machine learning workflow
• Ability to understand tools and frameworks such as Keras, PyTorch, and TensorFlow used by data scientists
• Experience deploying and serving LLMs using production-ready frameworks such as vLLM, TensorRT, TGI, or similar
• Experience using popular AIOps/MLOps frameworks such as MLFlow or Kubeflow
• Strong background in Software Engineering
• Strong in programming languages such as C, C++ and Python
• Strong communication skill

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