Machine learning engineer

1 week ago

Ballari, India TalentBridge Full time

Job Summary: As a Machine Learning Engineer specializing in generative AI, you will be responsible for designing, developing, and deploying advanced machine learning models that generate high-quality, human-like content. You will collaborate with cross-functional teams to integrate these models into our products, enhancing their capabilities and delivering exceptional value to our clients. Key Responsibilities: Develop, train, and optimize generative AI models for various applications, including text, image, and audio generation. Collaborate with product managers, software engineers, and data scientists to understand project requirements and deliver robust AI solutions. Conduct research to stay updated on the latest advancements in generative AI and apply best practices to improve model performance. Implement and maintain scalable machine learning pipelines for training and deploying models in production environments. Evaluate and fine-tune models to ensure they meet performance, accuracy, and efficiency standards. Perform data preprocessing, augmentation, and annotation to prepare high-quality datasets for model training. Troubleshoot and resolve issues related to model performance, data quality, and integration with other systems. Document model architecture, training processes, and performance metrics for internal and client-facing reports. Qualifications: Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field. Proven experience in developing and deploying generative AI models using frameworks such as Tensor Flow, Py Torch, or similar. Strong programming skills in Python and proficiency with machine learning libraries and tools. Familiarity with natural language processing (NLP), computer vision, and other relevant AI techniques. Experience with cloud platforms such as AWS, Google Cloud, or Azure for model deployment and management. Knowledge of data preprocessing, feature engineering, and model evaluation techniques. Excellent problem-solving skills and the ability to work independently and as part of a team. Strong communication skills to effectively convey technical concepts to non-technical stakeholders. Preferred Qualifications: Experience with GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and other advanced generative models. Understanding of ethical considerations and bias mitigation in AI systems. Contributions to open-source projects or publications in relevant conferences/journals. Experience with MLOps practices and tools for continuous integration and deployment of machine learning models.

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