If you are ready to lead the way in technology innovation at JPMorgan Chase, we’ve got an exciting opportunity for a technology leader like you to make a real difference in the industry.

As Director of Architecture within Connected Commerce Technology, you will lead architectural initiatives across multiple departments, collaborating with both business and technology teams. You will define and implement technical strategies that support critical business objectives and ensure alignment with industry trends and best practices. Your leadership will guide the adoption of scalable, secure, and innovative solutions across the organization.

Job responsibilities

• Define target-state architectures and roadmaps for data platforms, AI/ML platforms, and analytics enablement.
• Establish architectural patterns, standards, and decision records that scale across teams.
• Design robust data ingestion and transformation architectures for batch and streaming use cases, ensuring data quality, lineage, metadata, and observability.
• Develop data modeling standards for analytical and operational scenarios, including dimensional, wide table, and feature-oriented schemas.
• Integrate privacy and security controls into platform design, including data classification, access patterns, encryption, retention, auditability, and policy enforcement.
• Collaborate with risk and compliance teams to ensure platform capabilities support regulatory and governance requirements.
• Define end-to-end MLOps patterns, including feature engineering, model training, evaluation, registry, deployment strategies, monitoring, and drift detection.
• Architect online and offline inference paths with clear service level agreements and rollback strategies.
• Guide teams in building production-grade AI services, ensuring reproducibility, experiment tracking, and environment parity.
• Set non-functional requirements for throughput, latency, scalability, resilience, and cost, and drive capacity planning and performance testing.
• Lead architecture reviews for high-impact initiatives, mentor engineering teams, and serve as an escalation point for complex technical decisions and cross-team integration.

Required qualifications, capabilities, and skills

• Minimum of 10 years of applied experience in software engineering concepts, with formal training or certification, and at least 5 years leading technologists to manage and resolve complex technical challenges.
• Experience in software or data architecture roles with demonstrated ownership of large-scale data and/or machine learning platforms.
• Proficiency in data ingestion and processing systems, including batch and streaming, as well as data lakes, warehouses, and analytical compute environments.
• Knowledge of data governance, including metadata management, lineage, access controls, auditing, and data quality frameworks.
• Experience with machine learning lifecycle systems, such as training pipelines, model registry, deployment patterns, and monitoring.
• Understanding of distributed systems concepts, including partitioning, consistency, idempotency, and backpressure.
• Experience in data modeling for analytics and machine learning, including feature engineering patterns.
• Familiarity with CI/CD and infrastructure automation principles, as well as environment isolation and promotion workflows.

Preferred qualifications, capabilities, and skills

• Experience with real-time machine learning or decisioning systems, including low-latency inference and online/offline consistency.
• Familiarity with model risk management and governance practices, such as validation, explainability, and audit trails.
• Experience with data mesh and domain-oriented data product design.
• Experience building or governing generative AI platform patterns, including evaluation, safety controls, usage monitoring, and cost management.
• Background in regulated environments requiring strong controls, auditability, and change management.