Be part of a dynamic team where your distinctive skills will contribute to a winning culture and team.

  As a Applied AI/ML Engineer/Data Scientist at JPMorgan Chase within the Asset & Wealth Management, you serve as a seasoned member of an agile team to design and deliver trusted data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. You are responsible for developing, testing, and maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

 

Supports review of controls to ensure sufficient protection of enterprise data. Develop and implement AI/ML models and algorithms to solve business problems. Train and evaluate AI/ML models using large datasets. Advises and makes custom configuration changes in one to two tools to generate a product at the business or customer request. Apply advanced statistical and ML techniques, including predictive modeling, time series analysis, and optimization algorithms, to extract insights from complex data sets. Optimize and fine-tune AI/ML models for performance and accuracy. Design and develop data pipelines to preprocess and transform data for AI/ML models. Updates logical or physical data models based on new use cases Frequently uses SQL and understands NoSQL databases and their niche in the marketplace Adds to team culture of diversity, equity, inclusion, and respect

 

Required qualifications, capabilities, and skills

 

Formal training or certification on Data engineering concepts and 3+ years applied experience Strong programming skills in Python, with experience in developing and maintaining production-level code. Proficiency in working with large datasets and data preprocessing. Solid understanding of AI/ML algorithms and techniques, including deep learning, time series forecasting and natural language processing. Experience with cloud platforms, such as AWS for deploying and scaling AI/ML models. Experience with ETL tools such as Airflow. Preferred qualifications, capabilities, and skills   Experience in backend development, including databases (SQL/NoSQL/Graph), programming languages (Python/Java/Node.js), web frameworks, APIs, and microservices and possess front-end development skills, including HTML, CSS, and JavaScript  Knowledge of SRE practices. Experience working with AWS EKS, ECS, RDS and DynamoDB. Knowledge of large language models (LLMs) and accompanying toolsets the LLM ecosystem (e.g. Langchain, Vector databases, opensource Hugging Face Models. Exposure to cloud automation technologies such as Terraform