Job Summary:
Results-driven AI/ML Engineer with expertise in designing, training, and deploying scalable machine learning and deep learning models, with a strong focus on generative AI including transformer architectures. Skilled in developing and implementing advanced AI models for text, image, or multimodal generation, with experience in data preprocessing, model optimization, and MLOps integration. Adept at translating complex business challenges into AI-driven insights and automation while collaborating with cross-functional teams.
Key Responsibilities
- Design, develop, and deploy advanced AI models with a focus on generative AI, including transformer architectures (e.g., GPT, BERT, T5) and other deep learning models used for text, image, or multimodal generation
- Work with extensive and complex data sets, performing tasks such as cleaning, preprocessing, and transforming data to meet quality and relevance standards for generative model training
- Collaborate with cross-functional teams (e.g., product, engineering, data science) to identify project objectives and create solutions using generative AI tailored to business needs
- Implement, fine-tune, and scale generative AI models in production environments, ensuring robust model performance and efficient resource utilization
- Develop pipelines and frameworks for efficient data ingestion, model training, evaluation, and deployment, including A/B testing and monitoring of generative models in production
- Stay informed about the latest advancements in generative AI research, techniques, and tools, applying new findings to improve model performance, usability, and scalability
- Document and communicate technical specifications, algorithms, and project outcomes to technical and non-technical stakeholders, with an emphasis on explainability and responsible AI practices
Must Have Skills
- Generative AI (transformer architectures, GANs, VAEs)
- Deep learning frameworks (PyTorch, TensorFlow, Hugging Face Transformers)
- Python programming
- MLOps tools (Docker, Kubernetes, MLflow)
- Cloud platforms (AWS, GCP, Azure)
