Applied AI ML Lead, AI Agents & Agentic Systems

JPMorgan Chase

Confirmed live yesterday High trust

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Work type: On-site
Location: Palo Alto, CA
Posted: 6 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $212k

$150k most similar roles pay here $268k

This listing doesn't post a salary. Most similar roles pay $177,175–$246,150.

Based on 240 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 1098 open roles on FindRole.

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At a glance

TL;DR · Applied AI ML Lead, AI Agents & Agentic Systems

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As the Applied AI ML Lead - AI Agents & Agentic Systems within the AI and Machine Learning and Data Platform team in Corporate Sector, you will drive the design and delivery of machine learning and deep learning solutions to solve business problems. You will own the entire lifecycle from problem framing and experimentation through productionization, ensuring systems are robust and scalable. Your daily work involves conducting experiments to benchmark agentic techniques, optimizing system performance, and integrating Generative AI into the ML Platform. You will perform hands-on coding in Python to develop both proofs of concept and production-ready solutions. Key technical requirements include expertise in PyTorch or TensorFlow, LLMs, multi-agent orchestration, RAG, prompt engineering, and GPU optimization. You will also leverage skills in distributed training, search/ranking, and cloud platforms like AWS and SageMaker.

Skills

Machine Learning Deep Learning Large Language Models Generative AI Python PyTorch TensorFlow RAG Prompt Engineering Multi-agent Orchestration AWS SageMaker EKS Distributed Training Reinforcement Learning Meta Learning Search/Ranking Recommender Systems Graph Techniques

What you'll do

Design and deliver machine learning and deep learning solutions to solve complex business problems.
Manage the full lifecycle from problem framing and experimentation through productionization.
Develop end-to-end Python code for both experimental proofs of concept and production-ready systems.
Integrate Generative AI and Large Language Models (LLMs) into the existing ML Platform.
Conduct experiments to evaluate, benchmark, and tune advanced agentic systems and models.
Optimize system accuracy and performance by identifying and resolving technical bottlenecks.
Serve as a subject matter expert on ML algorithms, frameworks, and optimization techniques.
Drive decisions regarding product design, application functionality, and technical operations.

What we're looking for

MS and/or PhD in Computer Science, Machine Learning, or a related field.
At least 5 years of experience in applied machine learning.
At least 5 years of experience in programming languages such as Python (intermediate), Java, or C/C++.
At least 5 years of experience applying data science and ML techniques to solve business problems.
Solid background in LLMs and agentic applied science including multi-agent orchestration, reasoning, skills, and tools.
Expertise in deep learning frameworks such as PyTorch or TensorFlow.
Experience in advanced applied ML areas like GPU optimization, fine-tuning, RAG, and prompt engineering.
Experience with distributed training frameworks (preferred); experience with Search/Ranking, Recommender systems, or Graph techniques (preferred).

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