Lead Software Engineer – ML/Data Platform

Bring your Software Engineering experience to the next level and join The Machine Learning Center of Excellence (MLCOE) Team. 

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.
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

 Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Proficiency in Python coding 
• 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.
• 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.
• Using AWS Kinesis and Firehose for large-scale data ingestion and ETL with AWS Glue.
• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices

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