Firstsource Machine Learning Job Alerts 2025 | Hyderabad

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 TensorFlow, PyTorch, 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

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