The company is looking to integrate generative AI capabilities into its products and platforms, requiring expertise in architecting, training, fine-tuning, and deploying large generative models.
Requirements
- Expert-level proficiency in Python.
- Extensive experience with PyTorch (strongly preferred) and/or TensorFlow.
- Hands-on experience working with Large Language Models (e.g., via OpenAI API, Hugging Face Transformers, LangChain, LlamaIndex, or custom models).
- Deep understanding of NLP concepts (tokenization, embeddings, attention mechanisms).
- Proven experience with cloud platforms (AWS, GCP, or Azure) and MLOps tools (e.g., Docker, Kubernetes, MLflow, Weights & Biases, TFX).
- Strong analytical and problem-solving skills with the ability to iterate quickly from experimentation to production-ready solutions.
Responsibilities
- Architect, train, fine-tune, and optimize large generative models (e.g., LLMs like GPT, diffusion models like Stable Diffusion, VAEs, GANs) for specific use cases.
- Build robust, scalable data pipelines for pre-processing and curating massive training datasets.
- Implement and manage MLOps practices to deploy models into production, ensuring scalability, low latency, and high reliability. This includes containerization, API development, and continuous integration/continuous deployment (CI/CD).
- Apply advanced techniques like Retrieval-Augmented Generation (RAG), fine-tuning, quantization, and distillation to improve model efficiency, accuracy, and cost-effectiveness.
- Stay current with the latest academic research and open-source advancements in generative AI.
- Prototype new ideas and conduct experiments to validate their feasibility and impact.
- Work closely with product managers, data scientists, and software engineers to integrate generative AI capabilities into our products and platforms.
Other
- BACHELOR OF COMPUTER SCIENCE
- Collaboration
