Machine Learning- Model Implementation Engineer-Risk Management- Associate

Key Skills

Aartificial intelligence, AWS, CNNs, Deep Learning, Docker, Java Programming, JIRA, Kubernetes-K8s, Large Language Models - LLMs, Machine learning techniques, Natural Language Processing (NLP), NumPy, Pandas, Python Programming, PyTorch, RNNs, Scikit-learn, SPARK Programming, SQL, TensorFlow

Job Description

Quantitative model implementation team is looking for a Machine Learning Model Implementation Engineer to create and deliver efficient and scalable implementations for a variety of data intensive machine learning models including Natural Language Processing (NLP) and Large Language Models (LLMs), Deep Learning models like CNN and RNN, and other flavors of ML models. 

You will get an opportunity to stay at the forefront of technology, contributing to innovative solutions that make tangible impact in personal and professional growth while working together with the quantitative modelers that create advanced prototypes of the models and the technology core teams. This is a unique opportunity to apply your skills and have a direct impact on global business.

Candidates must possess strong analytical skills, strong aptitude with programming, knowledge of statistical concepts, have curiosity for data, and be familiar with ML Engineering and DevOps. 

Required qualifications, capabilities, and skills:

• Advanced degree in Computer Science, Data Science, Mathematics, Statistics, or related technical field.
• A minimum of 3 years of experience (minimum of 1 years with a Ph.D.) with machine learning techniques and advanced analytics and with deploying ML models in production environment (e.g. regression, classification, ranking, neural networks, or other models).
• Expertise with Python including NumPy and Pandas, proficiency with AWS, SQL, JIRA, Spark
• Experience with building and deploying ML models on AWS cloud.
• Exposure to the Deep Learning or Natural Language Processing (NLP) models

Preferred qualifications, capabilities, and skills:

• Ph.D. in a quantitative field.
• Proficiency with statistical methods including the goodness-of-fit tests and confidence intervals used in model performance evaluation
• AWS or Azure certified. Experience with Java, Kubernetes, Docker, Jules.
• Knowledge of designing Machine Learning Architecture framework
• Experience with Machine Learning and Deep Learning toolkits (e.g.: TensorFlow, PyTorch, Scikit-Learn) 
• Experience with software engineering best practices, such as version control, testing, and continuous integration and deployment
• Experience with distributed code and cross-platform operations / REST API is a plus.

ABOUT US

JPMorgan Chase & Co., one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.

We recognize that our people are our strength and the diverse talents and perspectives that they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. In accordance with applicable law, we make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as any mental health or physical disability needs. (If you are a US or Canadian applicant with a disability and wish to request an accommodation to complete the application process, please contact us by calling the Accessibility Line (US and Canada Only) 1-866-777-4690 and indicate the specifics of the assistance needed.)