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:
- Bachelors or Masters 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.
