Job Responsibilities
- Combines vast data assets with cutting-edge AI, including LLMs and Multimodal LLMs
- Bridges scientific research and software engineering, requiring expertise in both domains
- Leads, mentors, and inspires a team of 20 data scientists and engineers, fostering a culture of innovation and excellence
- Collaborates with senior leadership to identify opportunities for AI/ML to drive business growth and efficiency
Required qualifications, capabilities, and skills
- Masters or PhD in a quantitative discipline, e.g. Computer Science, Mathematics, Statistics.
- Proven track record in building and leading teams of experienced ML engineers/scientists, preferably in the financial sector.
- Scalable AI Model Deployment –end-to-end ML model lifecycle management, optimizing training, inference, and monitoring for large-scale AI applications.
- Generative AI & LLM Infrastructure – knowledge of AI ecosystems for LLMs (GPT, OpenAI), optimizing data ingestion, model scaling, and performance tuning.
- Python & C++ for AI Performance – Expertise in Python & C++ for building ML pipelines, optimizing AI inference, and ensuring low-latency execution.
- Expertise in AI ready infrastructure, data engineering, modern data management skills and platforms such as Databricks, SageMaker
- Ensure the scalability, reliability, and security of AI/ML systems.
- Ability to understand and align with business expectations, and write clear and concise OKRs (Objectives and Key Results).
- Excellent grasp of computer science fundamentals and SDLC best practices.
- Ability to understand business objectives and align ML problem definition.
- Excellent communication skills to effectively convey technical information and ideas at all levels, building trust with stakeholders.
Preferred qualifications, capabilities, and skills
- Stay abreast of industry trends and emerging technologies in AI/ML.