Company Overview Our client is at the forefront of leveraging cutting-edge artificial intelligence and machine learning technologies to drive investment strategies and generate alpha. We are seeking an exceptional AI Tech Lead to spearhead the technological innovation and AI implementation across quantitative research and trading operations. Job Description The AI Tech Lead will be responsible for designing, developing, and implementing advanced AI solutions that transform our investment decision-making processes. This role requires a unique blend of deep technical expertise, strategic thinking, and financial domain knowledge. Key Responsibilities Lead the architectural design and implementation of AI/ML systems for quantitative trading and investment research Develop and maintain sophisticated machine learning models for predictive analytics, market forecasting, and trading strategy optimization Architect scalable and high-performance AI infrastructure capable of processing vast amounts of financial data in real-time Collaborate closely with quantitative researchers, data scientists, and trading teams to translate complex financial insights into actionable AI solutions Implement robust machine learning pipelines for data preprocessing, model training, validation, and deployment Ensure the highest standards of model interpretability, explainability, and risk management Drive innovation in AI technologies, including large language models, deep learning, and advanced predictive algorithms Mentor and develop a team of AI engineers and data scientists Evaluate and integrate emerging AI technologies and research into existing systems Required Qualifications Ph.D. or Master's degree in Computer Science, Machine Learning, Statistics, or a related quantitative field 7 years of experience in AI/ML development, with at least 3 years in a leadership role Extensive experience in building production-grade machine learning systems Strong programming skills in Python, with expertise in ML frameworks such as TensorFlow, PyTorch, and scikit-learn Proven track record of developing predictive models for financial applications Deep understanding of machine learning algorithms, statistical modeling, and data processing techniques Experience with cloud computing platforms (AWS, GCP, Azure) Excellent understanding of distributed computing and big data technologies Technical Skills Advanced machine learning and deep learning techniques Natural language processing Time series analysis and forecasting Reinforcement learning Big data technologies (Spark, Hadoop) Cloud infrastructure and containerization (Docker, Kubernetes) Version control and CI/CD practices