Applied AI ML Lead

JPMorgan Chase

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Jersey City, NJ
Posted: 29 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $223k

$174k most similar roles pay here $272k

This listing doesn't post a salary. Most similar roles pay $192,050–$254,750.

Based on 239 similar postings.

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About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1117 open roles on FindRole.

Listed pay typically runs $186,160–$215,000 across 7 roles with salary data.

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At a glance

TL;DR · Applied AI ML Lead

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As the Applied AI ML Lead, Chief Data & Analytics Office, you will join a data science team to lead the design, development, and deployment of advanced AI, generative AI, and large language model solutions. You will serve as a subject matter expert on machine learning techniques while collaborating with cross-functional teams to deliver scalable, production-ready systems and ensure responsible AI governance. Your daily work involves end-to-end Python code development for both proof-of-concept and production environments, integrating generative AI into the ML platform, and optimizing system performance. You will utilize technologies including PyTorch, TensorFlow, Kubernetes, Ray, Slurm, and MLOps tools like MLflow. The role addresses complex business challenges within a regulated environment by implementing prompt engineering, agentic workflows, and distributed systems across cloud platforms such as AWS, Azure, or GCP to solve practical business problems.

Skills

Generative AI Large Language Models Python PyTorch TensorFlow Prompt Engineering Agentic Workflows MLOps CI/CD AWS Azure GCP Kubernetes Ray Slurm Sagemaker EKS High-Performance Computing

What you'll do

Design, develop, and deploy advanced AI, GenAI, and large language model solutions.
Serve as a subject matter expert on machine learning techniques and optimizations.
Develop end-to-end production-ready code in Python for both proof-of-concept and live systems.
Integrate generative AI into the ML platform using state-of-the-art techniques.
Drive the adoption of modern ML infrastructure, tools, and best practices.
Optimize system accuracy and performance by identifying and resolving technical inefficiencies.
Ensure responsible AI practices, model governance, and compliance with regulatory standards.
Mentor and guide other AI engineers and scientists to foster a culture of learning.

What we're looking for

Master’s or PhD in Computer Science, Engineering, Mathematics, or a related quantitative field.
Minimum 8 years of hands-on experience in applied machine learning, including generative AI, large language models, or foundation models.
At least 5 years of experience programming in Python and using ML frameworks like PyTorch or TensorFlow.
Proven experience designing, training, and deploying large-scale ML/AI models in production environments.
Deep understanding of prompt engineering, agentic workflows, and orchestration frameworks.
Experience with cloud platforms (AWS, Azure, GCP) and distributed systems (Kubernetes, Ray, Slurm).
Solid grasp of MLOps tools and practices including MLflow, model monitoring, and CI/CD for ML.
Strong communication skills to explain complex technical concepts to diverse audiences and demonstrated leadership among cross-functional teams.
Experience with high-performance computing, GPU infrastructure, or big data processing tools (preferred).
Advanced knowledge in reinforcement learning, meta learning, search/ranking, recommender systems, or graph techniques (preferred).
Background in financial services or regulated industries (preferred).
Published research or contributions to open-source GenAI/LLM projects (preferred).

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