Applied AIML Lead, Vice President

Testing Site - JPMC; Jersey City (NJ); On-site; USD 180,000 - 260,000; Full time

Job summary

Testing Site - JPMC in Jersey City seeks a Lead Applied AI/ML Data Scientist to shape AI strategy and deliver production-grade solutions for Asset & Wealth Management. You will partner with engineering, product, and business teams to architect, launch, and scale AI capabilities that drive client and employee value.

You will lead multimodal LLM projects, ensure robust ML Ops, and translate cutting-edge research into reliable production systems while mentoring teams in a fast-paced environment.

Qualifications

• PhD or equivalent in a quantitative field such as CS, DS, math, or statistics.
• Deep expertise in NLP, CV, knowledge graphs, reinforcement learning, or multimodal LLMs.
• Proven ability to design, deploy, and operate production ML pipelines.
• Experience with agentic patterns and enterprise workflows.
• Strong communication and stakeholder engagement skills.

Responsibilities

• 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.

Skills

NLP & CV expertise

Multimodal LLMs

ML pipeline design

Leadership & mentoring

Stakeholder communication

Production-grade ML systems

Education

PhD or equivalent in CS/DS/math

Tools

TensorFlow

PyTorch

LangChain

LangGraph

Auto-GPT

Job description

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.

Job Responsibilities

• 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.

Required Qualifications, Capabilities, and Skills

• 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., LangChain, LangGraph, Auto-GPT) and their application in enterprise workflows.
• Expertise with modern ML/DL toolkits (e.g., TensorFlow, PyTorch) and supporting ecosystems.
• Exceptional communication and stakeholder engagement skills; ability to convey complex concepts and build trust at all levels.

Preferred Qualifications, Capabilities, and Skills

• 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.