Applied AI ML Senior Associate -AI Agents & Agentic Systems

JPMorgan Chase & Co.•New York, NY

8h•$137,750 - $195,000

About The Position

Bring your applied research and machine learning skills to a team building modern, high-impact generative AI capabilities. You’ll work in a collaborative environment where experimentation, engineering excellence, and customer focus come together to deliver real outcomes. As an Applied AI ML Senior Associate at JPMorganChase within the Applied Artificial Intelligence and Machine Learning Data Platforms team in Corporate Sector, you will help design, develop, and productionize machine learning and deep learning solutions, including generative AI use cases. You will partner closely with product and engineering stakeholders to turn business needs into reliable models, workflows, and platform capabilities. You will contribute to a scalable platform approach that can support multiple business lines while maintaining strong standards for performance, quality, and usability.

Requirements

MS and/or PhD in Computer Science, Machine Learning, or a related field, with at least 2 years of applied machine learning experience.
At least 4 years' experience in one of the programming languages like Python, Java, C/C++, etc. Intermediate Python is a must.
At least 2 years’ experience in applying data science, ML techniques to solve business problems.
Solid background in Natural Language Processing (NLP) and Large Language Models (LLMs)
Hands-on 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, fine tuning, 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

Nice To Haves

Master’s degree in computer science, ML or related areas
Experience with Ray, MLFlow, and/or other distributed training frameworks.
In-depth understanding of Search/Ranking, Recommender systems, Graph techniques, and other advanced methodologies.
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.
Experience working with large-scale MLOps pipelines, working with and deploying models to production services.

Responsibilities

Serves as a subject matter expert on a wide range of ML techniques and optimizations.
Provides in-depth knowledge of ML algorithms, frameworks, and techniques.
Enhances ML workflows through advanced proficiency in large language models (LLMs) and related techniques.
Conducts experiments using latest ML technologies, analyzing results, tuning models
Provides has 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.
Optimizes 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.
Integrates Generative AI within the ML Platform using state-of-the-art techniques.