Applied AI ML [Multiple Positions Available]

JPMorgan Chase & Co.•Palo Alto, CA

13h•$177,500 - $260,000•Onsite

About The Position

This role involves building and training production-grade Machine Learning models on large-scale datasets to address various business use cases within Global Banking. Responsibilities include utilizing large-scale data processing frameworks for feature engineering, working with both structured and unstructured data, and applying Deep Learning models and Generative AI techniques for tasks such as multi-source data fusion, information retrieval, question-answering, forecasting, and anomaly detection. The role also requires building ML models across Public and Private clouds, including container-based Kubernetes environments, and developing end-to-end ML pipelines to transform existing applications and business processes into robust Artificial Intelligence systems. This includes building both batch and real-time model prediction pipelines with integrations into existing applications and front-end systems. Collaboration with teams to develop large-scale data modeling experiments, evaluate them against strong baselines, and extract key statistical insights and cause-and-effect relationships is also a key aspect of the position.

Requirements

Bachelor's degree in Computer Engineering, Computer Science, Information Systems, Data Science, Artificial Intelligence, Machine Learning, or related field of study plus 3 years (36 months) of experience in the job offered or as Applied AI ML Associate, Software Engineer, Software Engineering Program Analyst, or related occupation.
Master's degree in Computer Engineering, Computer Science, Information Systems, Data Science, Artificial Intelligence, Machine Learning, or related field of study plus 1 year (12 months) of experience in the job offered or as Applied AI ML Associate, Software Engineer, Software Engineering Program Analyst, or related occupation.
Managing and transforming structured and unstructured data using Pythonic implementations of normalization, aggregation, data cleaning, enrichment, entity extraction, or sentiment analysis.
Validating data quality and completeness.
Implementing anomaly detection methods and building robust, reusable features for AI/ML teams.
Conducting data analysis, modeling, and engineering tasks required in end-to-end AI/ML solution design and implementation using Python, SQL, and PySpark.
Exploring, visualizing, and analyzing data to create and validate proof-of-concept AI/ML engineering solutions.
Applying machine learning and statistical techniques including regression, classification, clustering, time series analysis, dimensionality reduction, and mathematical optimization to address business challenges.
Developing, evaluating, and training AI/ML models, taking ownership of the full implementation lifecycle including hyperparameter tuning, model selection, and performance evaluation.
Writing optimized code in SQL, Spark, and Python leveraging cloud technologies and cloud analytics platforms.
Designing and deploying scalable, distributed statistical and AI/ML models in production environments using containerization and CI/CD pipelines.
Building and maintaining end-to-end data pipelines for model deployment and operationalization, with embedded performance monitoring.
Communicating, documenting, and presenting machine learning results and insights to non-technical audiences.
Delivering AI/ML solutions with cross-functional teams such as Data Engineering, Model Operations, or Design.
Conducting research and development work to evaluate emerging AI/ML methods, algorithms, and best practices.
Translating academic research into use-case specific implementations and practical solutions.
Demonstrating proficiency in MLOps practices, model monitoring, automated retraining, data privacy and compliance in AI/ML workflows, version control systems, RESTful API development for model serving, distributed computing frameworks, and experiment tracking tools.

Responsibilities

Build and train production-grade Machine Learning on large-scale datasets to solve various business use cases for Global Banking.
Use large scale data processing frameworks for feature engineering and be proficient across various data, both structured and unstructured.
Use Deep Learning models and Generative AI techniques for solving various business use cases including multi-source data fusion, information retrieval, question-answering, forecasting and anomaly detection.
Build ML models across Public and Private clouds including container-based Kubernetes environments.
Develop end-to-end ML pipelines necessary to transform existing applications and business processes into robust Artificial Intelligence systems.
Build both batch and real-time model prediction pipelines with existing application and front-end integrations.
Collaborate to develop large-scale data modeling experiments, evaluating against strong baselines, and extracting key statistical insights and cause and effect relationships.