Join JPMorganChase’s MLCOE to turn cutting-edge AI into production at enterprise scale.
The Machine Learning Center of Excellence (MLCOE) team partners across the firm to create and share Machine Learning Solutions for our most challenging business problems. In this role you will work and collaborate with a team comprised of a multi-disciplinary community of experts focused exclusively on Machine Learning. On this team you will work with cutting-edge techniques in disciplines such as Deep Learning and Reinforcement Learning.
As a Lead Software Engineer at JPMorgan Chase within the Corporate Sector – AIML Data Platforms and Machine Learning Center of Excellence Team, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
- Designs, develops and maintains production grade software
- Engineers data pipelines to ingest and transform large volumes of data.
- Feeds processed data into machine learning model pipelines.
- Stores resulting data in data warehouses and data lakes.
- Design sand implements end-to-end machine learning model pipelines, from data input to serving outputs to a large user base.
- Deploys complete systems into production environments.
- Resolves production issues within a 5-hour SLA.
- Adds new features and maintains product code to ensure ongoing system performance and reliability.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Infrastructure design for large-scale machine learning model deployment using tools like Terraform or AWS Infrastructure as Code.
- Building metrics and setting up AWS CloudWatch monitors and alarms for infrastructure and application performance.
- Working with data lakes (Amazon S3) and data warehouses (AWS Redshift).
- Utilizing AWS services and CI/CD pipelines for deploying and maintaining machine learning applications.
- Developing dynamic, interactive dashboards with Tableau or Qlik Sense, including advanced visualization, ETL automation, and ODBC connectors.
- Data manipulation, structuring, design flow, and query optimization using SQL and Python.
- Processing large datasets with data containers, multithreading, and multiprocessing in PySpark and TensorFlow.
- Developing software or microservices deployed as REST APIs.
- Using AWS Kinesis and Firehose for large-scale data ingestion and ETL with AWS Glue.
- Developing and automating high-performance, large-scale data processing systems.
Preferred qualifications, capabilities, and skills
- Familiarity with recent large language model technologies
- Familiarity with engineering systems using large language models
- Familiarity with LLM tools such as Langchain or Haystack