Applied AI/ML Platform Engineer

JPMorganChase; New York (NY); On-site; USD 120,000 - 200,000; Full time

Job summary

JPMorganChase is seeking an Applied AI ML Associate to advance ML/DL solutions on a centralized AIML Data Platforms platform. You will contribute to state-of-the-art generative tech, collaborate with business lines, and translate experiments into production-ready code.

The role emphasizes end-to-end Python development, model tuning, and scalable ML workflows, with a focus on accuracy, performance, and cross-team collaboration in a dynamic corporate environment.

Qualifications

• Experience in Python, Java, C/C++, with strong Python skills.
• Solid background in NLP and large language models (LLMs).
• Hands-on experience with ML and deep learning methods.
• Deep understanding of PyTorch or TensorFlow.
• Experience in GPU optimization, fine tuning, embeddings, inference, and RAG.
• Ability to work independently with limited supervision.
• Detail oriented with excellent communication and teamwork.

Responsibilities

• Serves as SME on a wide range of ML techniques and optimizations.
• Provides in-depth knowledge of ML algorithms, frameworks, and techniques.
• Enhances ML workflows with advanced proficiency in LLMs and related techniques.
• Conducts experiments using latest ML technologies, analyzes results, tunes models.
• Owns end-to-end code development in Python for POC/experimentation and production solutions.
• Optimizes system accuracy and performance; collaborates with product and engineering teams.
• Integrates Generative AI within the ML Platform using state-of-the-art techniques.

Skills

Python

NLP

LLMs

Deep Learning

GPU optimization

Prompt engineering

Team player

Communication

Education

Master’s degree in Computer Science/ML

Tools

PyTorch

TensorFlow

Job description

JPMorganChase is seeking an Applied AI ML Associate to advance ML/DL solutions on a centralized AIML Data Platforms platform. You will contribute to state-of-the-art generative tech, collaborate with business lines, and translate experiments into production-ready code.

The role emphasizes end-to-end Python development, model tuning, and scalable ML workflows, with a focus on accuracy, performance, and cross-team collaboration in a dynamic corporate environment.