Director of Artificial Intelligence

Role Overview: You will be responsible for designing, building, and deploying end-to-end AI/ML systems in production. Your expertise in data science, including statistical analysis, experimentation, and data storytelling, will be crucial in working with large-scale, real-world datasets for model training and analysis. You should be comfortable navigating urgency, ambiguity, and fast-changing priorities while independently solving complex ML problems from idea to implementation. Additionally, your strong leadership experience building and guiding high-performing AI teams will be essential, along with hands-on experience in deep learning, NLP, LLMs, and classical ML techniques. Key Responsibilities: - Design, build, and deploy end-to-end AI/ML systems in production - Demonstrate expertise in data science, including statistical analysis, experimentation, and data storytelling - Work with large-scale, real-world datasets for model training and analysis - Navigate urgency, ambiguity, and fast-changing priorities effectively - Solve complex ML problems independently, from idea to implementation - Lead and guide high-performing AI teams - Hands-on experience with deep learning, NLP, LLMs, and classical ML techniques - Conduct model experimentation, tuning, optimisation, and evaluation at scale - Utilize solid software engineering background while working with data and full-stack teams - Implement production-grade ML pipelines on cloud platforms (GCP, AWS, Azure) Qualifications Required: - PhD in Computer Science, Data Science, Machine Learning, AI, or a related field - Strong programming skills in Python (preferred), with experience in Java, Scala, or Go as a plus - Deep expertise with ML frameworks like TensorFlow, PyTorch, Scikit-learn - Experience with large-scale datasets, distributed data processing (e.g. Spark, Beam, Airflow) - Proficient in NLP, deep learning, and working with transformer-based LLMs - Experience with MLOps practices (CI/CD, model serving, monitoring, and lifecycle management) - Hands-on experience with cloud platforms (GCP, AWS, Azure) and tools like Vertex AI, SageMaker, or Databricks - Strong grasp of API design, system integration, and delivering AI features in full-stack products - Comfortable working with SQL/NoSQL and data warehouses like BigQuery or Snowflake - Familiarity with ethical AI, model explainability, and secure, privacy-aware AI development,