Senior Machine Learning Engineer – MLOps, VertexAI, LLMs, GenAI, ML Model Management
1 week ago
- Managing the deployment and maintenance of machine learning models in production environments and ensuring seamless integration with existing systems.
- Monitoring model performance using metrics such as accuracy, precision, recall, and F1 score, and addressing issues like performance degradation, drift, or bias.
- Troubleshoot and resolve problems, maintain documentation, and manage model versions for audit and rollback.
- Analyzing monitoring data to preemptively identify potential issues and providing regular performance reports to stakeholders.
- Optimization of the queries and pipelines.
- Modernization of the applications whenever required
- Expertise in programming languages like Python, SQL
- Solid understanding of best MLOps practices and concepts for deploying enterprise level ML systems.
- Understanding of Machine Learning concepts, models and algorithms including traditional regression, clustering models and neural networks (including deep learning, transformers, etc.)
- Understanding of model evaluation metrics, model monitoring tools and practices.
- Experienced with GCP tools like BigQueryML, MLOPS, Vertex AI Pipelines (Kubeflow Pipelines on GCP), Model Versioning & Registry, Cloud Monitoring, Kubernetes, etc.
- Solid oral and written communication skills and ability to prepare detailed technical documentation of new and existing applications.
- Strong ownership and collaborative qualities in their domain. Takes initiative to identify and drive opportunities for improvement and process streamlining.
- Bachelor's Degree in a quantitative field of mathematics, computer science, physics, economics, engineering, statistics (operations research, quantitative social science, etc.), international equivalent, or equivalent job experience.
- Experience in Azure MLOPS,
- Familiarity with Cloud Billing.
- Experience in setting up or supporting NLP, Gen AI, LLM applications with MLOps features.
- Experience working in an Agile environment, understanding of Lean Agile principles.
UPS
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