You’re ready to gain the skills and experience needed to grow within your role and advance your career — and we have the perfect software engineering opportunity for you.

As a Software Engineer II at JPMorgan Chase within the Corporate Sector - Technology Machine Learning team, you are part of an agile team that works to enhance, design, and deliver the software components of the firm’s state-of-the-art technology products in a secure, stable, and scalable way. As an emerging member of a software engineering team, you execute software solutions through the design, development, and technical troubleshooting of multiple components within a technical product, application, or system, while gaining the skills and experience needed to grow within your role.

JPMorgan Chase is dedicated to addressing complex business challenges through the application of data science and machine learning techniques across Risk, Compliance, Conduct, and Operational Risk. As an Applied AI/ML Engineer on the team, you will have the opportunity to explore intricate business problems and apply advanced algorithms to develop, test, and evaluate AI/ML applications or models for these challenges. You will leverage the firm’s extensive data resources from both internal and external sources using Python, Spark, and AWS, among other systems. You are expected to extract business insights from technical results and effectively communicate them to a non-technical audience.

Job responsibilities

• Design and architect end to end solutions in AI domain ranging from Anomaly detection Use cases, Chat with your at data, and using GenAI.
• Proactively develop an understanding of key business problems and processes.
• Execute tasks throughout the model development process, including data wrangling/analysis, model training, testing, and selection.
• Generate structured and meaningful insights from data analysis and modelling exercises, and present them in an appropriate format according to the audience.
• Collaborate with other data scientists and machine learning engineers to deploy machine learning solutions.
• Conduct ad-hoc and periodic analysis as required by business stakeholders, the model risk function, and other groups.
• Adds to team culture of diversity, equity, inclusion, and respect
• Applies knowledge of tools within the Software Development Life Cycle toolchain to improve the value realized by automation
• Applies technical troubleshooting to break down solutions and solve technical problems of basic complexity
• Learns and applies system processes, methodologies, and skills for the development of secure, stable code and systems

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 2+ years applied experience post-advanced degree (MS, PhD) in a quantitative field (e.g., Data Science, Computer Science, Applied Mathematics, Statistics, Econometrics).
• Hands on experience in statistical inference and experimental design (such as probability, linear algebra, calculus).
• Deep knowledge and experience in Data wrangling: understanding complex datasets, cleaning, reshaping, and joining messy datasets using Python.
• Practical expertise and work experience with ML projects, both supervised and unsupervised.
• Proficient programming skills with Python, including libraries, such as, NumPy, pandas, and scikit-learn
• Understanding and usage of the OpenAI API
• Knowledge and understand in NLP: tokenization, embeddings, sentiment analysis, basic transformers for text-heavy datasets.
• Experience with LLM & Prompt Engineering, including tools like LangChain, LangGraph, and Retrieval-Augmented Generation (RAG).
• Experience in anomaly detection techniques, algorithms, and applications.

Preferred qualifications, capabilities, and skills

• Experience with deep learning frameworks such as TensorFlow and PyTorch,
• Experience with big data frameworks, with a preference for Databricks.
• Experience with databases, including SQL (Oracle, Aurora), and Vector DB.
• Experience working with engineering teams to operationalize machine learning models.