Machine Learning Operations Engineer

Responsibilities

• Design, build, and deploy robust ML pipelines for training, fine-tuning, and inference of models (NLP-focused: NER, Classification).
• Develop and maintain CI/CD workflows for ML pipelines using Jenkins or similar tools, ensuring rapid and safe deployment to production.
• Implement model monitoring and alerting systems to track performance degradation and drift in real-time.
• Collaborate with cross-functional teams to retrain models on trigger events and integrate feedback loops into the ML lifecycle.
• Hands on with Helm deployment of ML Pipelines in Kubernetes cluster and optimize for scalable and resilient operations.
• Use MLflow, Kubeflow, and related tools for experiment tracking, model versioning, and reproducibility.
• Write clean, efficient, and scalable code in Python using frameworks such as PyTorch and CUDA.
• Experience with tuning, optimising LLM Applications performance in production.
• Required Skills:
• Strong programming experience in Python and PyTorch.
• Hands-on experience with CI/CD pipelines using Jenkins.
• Proficient with Kubernetes for deploying and managing ML workloads.
• Experience with model training, fine-tuning, and inference pipeline development.

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Company Intel

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