Must-Have:
- A fundamental understanding of ML and DL principles, algorithms, and neural network architectures is critical.
- Proficient in Python and experienced with AI development tools like TensorFlow and PyTorch.
- Familiarity with techniques like Generative Adversarial Networks and Variational Autoencoders, and specific applications in text (NLP) and image generation.
- Expertise in text generation and working with Large Language Models (LLMs) is essential for many Gen AI applications.
- Skills in data preprocessing, feature engineering, and understanding of MLOps (CI/CD pipelines, deployment) are important for practical implementation.
Good-to-Have:
- A clear understanding of the role's purpose, highlight specific skills like Python, deep learning, NLP, and expertise in generative models such as GANs and VAEs, and emphasize a research mindset and strong problem-solving abilities, while also being adaptable and attractive to potential candidates
Responsibility of / Expectations from the Role:
- Design, develop, and implement generative AI models, such as GANs (Generative Adversarial Networks) and LLMs (Large Language Models), using deep learning techniques.
- Train models on large-scale datasets and fine-tune them for improved performance, efficiency, and scalability.
- Prepare and preprocess large datasets for training generative models.
- Collaborate with software engineers to integrate generative AI models into production environments and systems.
- Continuously research and stay updated on the latest advancements in generative AI, deep learning, and related fields to incorporate new techniques.
- Mentoring and guiding junior team members while assigning and tracking the tasks for closure without any schedule, quality and cost slippages
- Collaborate with backend, QA, UI/UX, and business teams to ensure seamless application integration and performance.
- Enforce best practices for browser compatibility, code reusability, localization (Japanese/English), and front-end security
- Integrate APIs and backend services to support dynamic insurance data, calculations, and transactions.
- Participate in code reviews, technical documentation, and knowledge sharing.