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 Associate at JPMorgan Chase within Corporate Technology, you are part of an agile team that  is dedicated to addressing complex business challenges using innovative AL/ML solutions to provide value across Risk, Compliance, Conduct, and Operational Risk. This role offers an opportunity to explore intricate business problems and apply advanced algorithms to develop, test, and evaluate AI/ML applications or models to extract business insights from technical results and effectively communicate them to a non-technical audience.

Job responsibilities

Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems including data wrangling/analysis, model training, testing, and selection. Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture Collaborate with other data scientists and machine learning engineers to deploy machine learning solutions. Adds to team culture of diversity, equity, inclusion, and respect

Required qualifications, capabilities, and skills

Formal training or certification on software engineering concepts and applied experience Relevant experience post-advanced degree (MS, PhD) in a quantitative field (e.g., Data Science, Computer Science, Applied Mathematics, Statistics, Econometrics). Hands-on practical experience and work experience with ML projects, both supervised and unsupervised. Proficient programming skills with Python, including libraries such as NumPy, pandas, and scikit-learn, as well as R. Understanding and usage of the OpenAI API. Experience with LLM & Prompt Engineering, including tools like LangChain, LangGraph, and Retrieval-Augmented Generation (RAG). Experience with data and model serving using Flask, FastAPI, and Kubernetes. Familiarity with other tools such as agent-based modeling frameworks (e.g., Reflection), Terraform, and Airflow. Demonstrated experience working with large and complex datasets. Experience in anomaly detection techniques and applications. Excellent problem-solving, communication (verbal and written), and teamwork skills.

Preferred qualifications, capabilities, and skills

Knowledge of deep learning frameworks such as TensorFlow and PyTorch. Understanding of big data frameworks such as Spark, Hadoop, or Databricks. Databases understanding, including SQL (Oracle, Aurora), Chroma DB, and Vector DB. Knowledge with graph analytics and neural networks. Familiarity in the financial services industry.