Applied AIML Lead, Vice President

Overview

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.