Applied AI ML Lead - AI Agents & Agentic Systems

JPMorgan Chase & Co.•Palo Alto, CA

8h•$171,000 - $260,000

About The Position

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.

Requirements

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 Natural Language Processing (NLP) and Large Language Models (LLMs)
Experience with machine learning and deep learning methods.
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).
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.

Nice To Haves

Experience with Ray, MLFlow, and/or other distributed training frameworks.
In-depth understanding of Search/Ranking, Recommender systems, Graph techniques, and other advanced methodologies.
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.

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.
Conducting experiments using latest ML technologies, analyzing results, tuning models
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.