Applied AIML Lead, Vice President

Job Description
As a Lead Applied AI/ML Data Scientist within Asset & Wealth Management’s, you will leverage deep technical expertise and inclusive leadership to shape AI strategy and deliver high-impact, production-grade solutions. You’ll partner across engineering, product, and business teams to architect, launch, and scale AI capabilities that drive client and employee value.

• Provide technical leadership, mentoring, and coaching; foster an inclusive, growth-mindset culture.
• Architect and ship production multimodal LLM systems across text, image, speech, and video.
• Design and implement GenAI and agentic solutions to automate complex operational workflows.
• Own end-to-end delivery, including architecture, performance, reliability, monitoring, and continuous improvement.
• Bridge cutting-edge AI research with robust engineering practices to build production-ready solutions.
• Establish best practices for ML Ops (evaluation, observability, testing, governance) to ensure safe and responsible deployment.
• Collaborate with cross-functional stakeholders and senior leadership; influence direction with clear, data-driven narratives.
• Drive results with an entrepreneurial, outcomes-focused mindset in a fast-paced environment.

• PhD or equivalent experience in Computer Science, Data Science, Mathematics, Statistics, or a related quantitative field.
• Strong background in NLP, Computer Vision, Knowledge Graphs, Reinforcement Learning, and/or multimodal LLMs; solid foundation in statistics, optimization, and ML theory.
• Advanced proficiency in designing, deploying, and operating production ML pipelines and services.
• Practical knowledge of agentic patterns and frameworks (e.g., Lang Chain, Lang Graph, Auto-GPT) and their application in enterprise workflows.
• Expertise with modern ML/DL toolkits (e.g., Tensor Flow, PyTorch) and supporting ecosystems.
• Exceptional communication and stakeholder engagement skills; ability to convey complex concepts and build trust at all levels.

• Familiarity with AWS cloud services and building scalable AI solutions in cloud environments.
• Experience with advanced agentic workflow orchestration: multi-agent coordination, stateful task management, and integration with event-driven architectures.
• Hands-on experience with parameter-efficient fine-tuning (LoRA, QLoRA, IA3), model quantization (INT8, FP16, GPTQ), and quantization-aware training for LLMs at scale.
• Deep knowledge of distributed training strategies (data/model/pipeline parallelism), memory optimization, and inference acceleration for large-scale multimodal models.