Description We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As an Applied AI ML Lead at JPMorgan Chase within the Asset Management Machine Learning Engineering team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. You will partner with our Global Data Science teams to design, develop, deploy and operate machine learning driven applications and data pipelines. Job responsibilities Building and operating highly sophisticated machine learning applications Designing production APIs & data delivery processes Integrating unstructured and timeseries data into production pipelines Collaborating with Devops engineers to plan and deploy data storage and processing systems, especially for text, timeseries or financial data Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems Develops secure high-quality production code, and reviews and debugs code written by others Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies Adds to team culture of diversity, equity, inclusion, and respect Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and advanced applied experience Experience building and operating pipelines for processing and ML inference for text, timeseries or financial data Advanced in one or more programming language(s); strong preference for Python Practical cloud native experience History of successfully collaborating with internal stakeholders and clarifying requirements Proven ability to iterate quickly Proficient in all aspects of the Software Development Life Cycle Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.) Preferred qualifications, capabilities, and skills Experience working with Kubernetes, Airflow (or similar schedulers) and Machine Learning frameworks Knowledge of Machine Learning algorithms such as common Deep Learning based Natural Language Processing (NLP) and Unsupervised Clustering Experience working with ElasticSearch
