Applied AI ML Lead - AI Agents & Agentic Systems

JPMorganChase; Palo Alto (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

JPMorganChase seeks an Applied AI and Machine Learning Lead to shape machine learning solutions at scale within the AI and ML Data Platform team in Corporate Sector. You will own problem framing, experimentation, deployment, and continuous improvement, mentoring engineers and aligning with product goals.

Your role emphasizes robust, scalable ML/DL systems, with hands-on Python development and expertise in LLMs, agentic frameworks, and GPU optimization to drive measurable business impact.

Qualifications

• MS and/or PhD in Computer Science, Machine Learning, or a related field, with at least 5 years of applied machine learning experience.
• At least 5 years' experience in one of the programming languages like Python, Java, C/C++, etc. Intermediate Python is a must.
• At least 5 years' experience in applying data science, ML techniques to solve business problems.
• Solid background in Large Language Models (LLMs) and agentic applied science such as multi-agent orchestration, reasoning, skills, tools.
• Experience with applied research and experimentation on machine learning and deep learning methods (including LLMs/GenAI).
• Deep understanding and expertise in deep learning frameworks such as PyTorch or TensorFlow.

Responsibilities

• Serve as a subject matter expert on a wide range of ML techniques and optimizations.
• Provide in-depth knowledge of ML algorithms, frameworks, and techniques.
• Enhance ML workflows through advanced proficiency in large language models (LLMs) and related techniques.
• Conduct experiments to evaluate and benchmark latest AI and agentic techniques, analyzing results, tuning models and agentic systems.
• Hands on coding to bring the experimental results into production solutions by collaborating with engineering team. Owning end to end code development in python for both proof of concept/experimentation and production-ready solutions.
• Optimizing system accuracy and performance by identifying and resolving inefficiencies and bottlenecks. Collaborates with product and engineering teams to deliver tailored, science and technology-driven solutions.
• Integrate Generative AI within the ML Platform using state-of-the‑art techniques.
• Drives decisions that influence the product design, application functionality, and technical operations and processes.

Skills

Python

Java

C/C++

ML techniques

LLMs

Team leadership

Communication

Problem solving

Education

MS/PhD in CS or related field

Tools

PyTorch

TensorFlow

SageMaker

EKS

Job description

Job Description

Join a team where your work directly shapes how machine learning is applied at scale across the firm. You'll partner with product, engineering, and data teams to take ideas from experimentation through production, improving outcomes through thoughtful model development, evaluation, and operational excellence.

As an Applied AI and Machine Learning Lead at JPMorganChase 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 that solve meaningful business problems. You will take ownership from problem framing and experimentation through productionization, ensuring solutions are robust, scalable, and measurable. You will also help raise the technical bar through mentorship, strong engineering practices, and a culture of continuous learning.

Job Responsibilities

• Serve as a subject matter expert on a wide range of ML techniques and optimizations.
• Provide in-depth knowledge of ML algorithms, frameworks, and techniques.
• Enhance ML workflows through advanced proficiency in large language models (LLMs) and related techniques.
• Conduct experiments to evaluate and benchmark latest AI and agentic techniques, analyzing results, tuning models and agentic systems.
• Hands on coding to bring the experimental results into production solutions by collaborating with engineering team. Owning end to end code development in python for both proof of concept/experimentation and production-ready solutions.
• Optimizing system accuracy and performance by identifying and resolving inefficiencies and bottlenecks. Collaborates with product and engineering teams to deliver tailored, science and technology-driven solutions.
• Integrate Generative AI within the ML Platform using state-of-the‑art techniques.
• Drives decisions that influence the product design, application functionality, and technical operations and processes.

Required Qualifications, Capabilities, And Skills

• MS and/or PhD in Computer Science, Machine Learning, or a related field, with at least 5 years of applied machine learning experience.
• At least 5 year's experience in one of the programming languages like Python, Java, C/C++, etc. Intermediate Python is a must.
• At least 5 years' experience in applying data science, ML techniques to solve business problems.
• Solid background in Large Language Models (LLMs) and agentic applied science such as mutli-agent orchestration, reasoning, skills, tools.
• Experience with applied research and experimentation on machine learning and deep learning methods (including LLMs/GenAI).
• Deep understanding and expertise in deep learning frameworks such as PyTorch or TensorFlow.
• Experience in advanced applied ML areas such as GPU optimization, finetuning, embedding models, inferencing, prompt engineering, evaluation, RAG (Similarity Search), reasoning, context management, and other advanced agentic capabilities.
• Ability to work on tasks and projects through to completion with limited supervision.
• Passion for detail and follow through. Excellent communication skills and team player
• Demonstrated leadership in working effectively with engineers, product managers, and other ML practitioners.

Preferred Qualifications, Capabilities, And Skills

• Experience with distributed training frameworks.
• In-depth understanding of advanced methodologies such as Search/Ranking, Recommender systems, Graph techniques, multi-agent orchestration, evaluation, benchmarking.
• Advanced knowledge in Reinforcement Learning or Meta Learning.
• Deep understanding of Large Language Model (LLM) techniques, including Agents, Planning, Reasoning, and other related methods.
• Experience with building and deploying ML models on cloud platforms such as AWS and AWS tools like Sagemaker, EKS, etc.