Join the Chief Data & Analytics Office (CDAO) at JPMorgan Chase and be part of a team that accelerates the firm's data and analytics journey. We focus on ensuring data quality and security while leveraging insights to promote decision-making and support commercial goals through AI and machine learning.
As an AI ML Lead Software Engineer within the Chief Data & Analytics Office, you will become part of a mission to modernize compliance through scalable and explainable AI. We are building a system that answers the question: “Can I use this data?”, not with guesswork, but with prediction/classification, logic, proof, and intelligent automation.
Our work sits at the intersection of applied machine learning, AI reasoning systems, and data governance. We are designing the triage layer of an intelligent decision engine that combines ML-driven classification, LLM-assisted parsing, and formal logic-based verification. This is an opportunity to tackle complex, ambiguous problems that touch every part of the firm’s data ecosystem and to build ML solutions that actually make decisions.
Job Responsibilities:
Architect and develop scalable Python-based systems that support ML-driven risk classification, tagging, and approval triageIntegrate ML models into microservices and APIs for use within AI Judge workflowsLead engineering design reviews, establish coding standards, and ensure system robustness and securityBuild and maintain feature pipelines and model-serving infrastructure using cloud-native toolsWork closely with ML scientists, data engineers, and product managers to align on requirements and delivery timelinesDrive engineering quality, CI/CD integration, observability, and unit testing for AI-enabled software componentsMentor junior engineers and uphold engineering excellence across the team
Required Qualifications, Capabilities, and Skills:
Master's degree in computer science, Software Engineering, or related field6+ years of experience as a backend or AI/ML software engineerProficiency in Python with deep experience in building distributed and containerized services (e.g., Flask/FastAPI, Docker, Kubernetes)Strong understanding of ML deployment workflows, feature engineering, and serving architecturesExperience building and deploying APIs and ML inference services in productionFamiliarity with ML model management, versioning, and performance monitoringStrong engineering fundamentals: data structures, system design, testing, and performance optimizationExcellent communication and collaboration skills across technical and non-technical teams
Preferred Qualifications, Capabilities, and Skills:
Experience with AWS cloud stack (S3, SageMaker, Lambda, ECS, etc.)Experience working with structured data, tabular models, and metadata-driven platformsExperience with regulated data systems, enterprise controls, or secure data processing workflowsContributions to open-source ML or backend tooling frameworks