We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. 

As an AI/ML Python Engineer at JPMorgan Chase within the Client Onboarding and KYC Engineering team, your responsibilities will include probing intricate business problems and employing sophisticated algorithms to design, evaluate, and implement AI/ML applications or models to resolve these issues. You will be required to leverage the company's extensive data assets from both internal and external sources using tools like Python, Spark, and AWS. Additionally, your role will encompass extracting business insights from technical results and effectively communicating them to a non-technical audience.

Job Responsibilities

Design and architect end to end solutions in AI domain, from Pattern matching, Chatbot implementation, 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.

Required qualifications, capabilities, and skills

Formal training or certification on software engineering concepts and advanced applied experience.Experience in statistical inference and experimental design (such as probability, linear algebra, calculus).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, as well as R.Understanding and usage of the OpenAI API.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.Excellent problem-solving, communication (verbal and written), and teamwork skills.

Preferred qualifications, capabilities, and skills

Experience with big data frameworks, with a preference for Databricks.Experience with databases, including SQL (Oracle, Aurora), and Vector DB.Familiarity with version control systems such as Bitbucket and GitHub.Experience with graph analytics and neural networks.Experience working with engineering teams to operationalize machine learning models.