Developer (Gen AI)

Must-Have:

1. A fundamental understanding of ML and DL principles, algorithms, and neural network architectures is critical.
2. Proficient in Python and experienced with AI development tools like TensorFlow and PyTorch.
3. Familiarity with techniques like Generative Adversarial Networks and Variational Autoencoders, and specific applications in text (NLP) and image generation.
4. Expertise in text generation and working with Large Language Models (LLMs) is essential for many Gen AI applications.
5. Skills in data preprocessing, feature engineering, and understanding of MLOps (CI/CD pipelines, deployment) are important for practical implementation.

Good-to-Have:

1. 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:

1. Design, develop, and implement generative AI models, such as GANs (Generative Adversarial Networks) and LLMs (Large Language Models), using deep learning techniques.
2. Train models on large-scale datasets and fine-tune them for improved performance, efficiency, and scalability.

3. Prepare and preprocess large datasets for training generative models.
4. Collaborate with software engineers to integrate generative AI models into production environments and systems.
5. Continuously research and stay updated on the latest advancements in generative AI, deep learning, and related fields to incorporate new techniques.
6. Mentoring and guiding junior team members while assigning and tracking the tasks for closure without any schedule, quality and cost slippages
7. Collaborate with backend, QA, UI/UX, and business teams to ensure seamless application integration and performance.
8. Enforce best practices for browser compatibility, code reusability, localization (Japanese/English), and front-end security
9. Integrate APIs and backend services to support dynamic insurance data, calculations, and transactions.
10. Participate in code reviews, technical documentation, and knowledge sharing.