Machine Learning Engineer
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1.Developing and implementing ML algorithms: The Machine Learning Engineer develops and implements machine learning algorithms to solve specific problems, such as natural language processing, computer vision, or predictive modeling
.2.Building data pipelines: The Machine Learning Engineer is responsible for building data pipelines that collect, store, and preprocess data used in machine learning algorithms.
3.Creating and maintaining ML infrastructure: The Machine Learning Engineer is responsible for creating and maintaining ML infrastructure, including hardware, software, and cloud platforms, that support the development and deployment of ML models.
4.Testing and validating ML models: The Machine Learning Engineer tests and validates ML models, ensuring that they are accurate, robust, and scalable
5.Troubleshooting ML systems: The Machine Learning Engineer troubleshoots ML systems, identifying and resolving issues related to performance, accuracy, and scalability
6.Deploying ML models: The Machine Learning Engineer deploys ML models in production environments, integrating them with other software systems and ensuring that they are reliable and scalable.
Job ID: 108885411
Skills:
Ml, Nlp, Pytorch, modern training infrastructure, Speech, Hugging Face, Llm
Skills:
Deep Learning, AWS, Pytorch, Python, Kubernetes, Azure, Gcp, Nlp, Spark, Video, Llm, feature engineering, Vision, fine-tuning, cloud platforms, embeddings, training pipelines, Production Monitoring, RAG, inference, end-to-end ML systems, VLM, distributed data processing, generative AI
Skills:
Apis, Tensorflow, Azure ML, Pytorch, Docker, System Design, Microsoft Azure, Kubernetes, Python, LangChain, ASR, Speech AI, Generative AI, MLflow, ML lifecycle management, Azure OpenAI, Machine learning fundamentals, TTS, CI CD pipelines, Data pipelines, Scalability and reliability
Skills:
Tensorflow, Machine Learning, Python, Deployment monitoring, LLMs, Retraining, Generative AI systems, Pydantic, Drift detection, Scikit-learn, Time-series modeling, anomaly detection, Agent-based architectures, Data pipelines, Production ML systems, ML frameworks, PTorch, Feature engineering, Applied AI, Forecasting, Embeddings
Skills:
Python, Machine Learning, Artificial Intelligence, REST, cloud, Tensorflow, gRPC services, MLOps tools, data pipelines
