Position Summary
As the Chief AI Scientist, you will spearhead the development and deployment of cutting‑edge AI technologies, particularly in LLM and NLP domains. Reporting directly to the CTO/CEO, you will drive strategic AI research, foster innovation, and collaborate with cross‑functional teams to integrate AI solutions that enhance our products and services. This role demands a blend of technical expertise, leadership, and a passion for pushing the boundaries of AI.
Key Responsibilities
- Lead the AI research and development team, setting the vision and roadmap for LLM and NLP projects, including model training, fine‑tuning, and optimization.
- Design and implement advanced LLM architectures (e.g., based on transformers like GPT, BERT, or similar) for applications such as natural language understanding, generation, sentiment analysis, and conversational AI.
- Oversee NLP initiatives, including text processing, entity recognition, machine translation, and semantic analysis, ensuring scalability and ethical AI practices.
- Collaborate with engineering, product, and data teams to deploy AI models into production, focusing on performance metrics like accuracy, latency, and resource efficiency.
- Mentor junior scientists and engineers, fostering a culture of innovation and continuous learning.
- Evaluate and integrate emerging AI tools, frameworks, and datasets to accelerate development.
- Ensure compliance with AI ethics, bias mitigation, data privacy regulations (e.g., GDPR), and responsible AI deployment.
- Partner with external stakeholders, including academia and industry leaders, to drive collaborative AI projects.
Required Qualifications
- PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field; or equivalent professional experience.
- 10+ years of experience in AI research and development, with at least 5 years in leadership roles focused on LLM and NLP.
- Proven track record of developing and deploying production‑grade LLM/NLP models, demonstrated through publications, patents, or successful projects.
- Deep expertise in machine learning frameworks (e.g., TensorFlow, PyTorch, Hugging Face Transformers) and NLP libraries (e.g., spaCy, NLTK).
- Strong understanding of deep learning architectures, including attention mechanisms, reinforcement learning from human feedback (RLHF), and multimodal integration.
- Experience with large‑scale data handling, cloud computing (e.g., AWS, GCP), and distributed training systems.
- Excellent leadership and communication skills, with the ability to translate complex technical concepts to non‑technical stakeholders.
Do note that we will only be in touch if your application is shortlisted.