Lead Software Engineer - AI Governance Engineering

Join a team where your engineering work accelerates how machine learning is built, governed, and delivered across JPMorgan Chase. You will help create platform capabilities that make it easier for teams to move from ideas to production with confidence. If you enjoy building scalable systems and improving developer experience, this role offers meaningful impact and career growth.

As a Lead software engineer at JPMorgan Chase within the Corporate Artificial Intelligence and Machine Learning Data Platforms team, you will build and enhance products that support the machine learning lifecycle-spanning model operations, data development (such as processing and data annotation), and governance tooling. You will collaborate closely with engineers, system architects, product managers, data scientists, and research partners to deliver reliable, secure, and user-friendly platform services.

Job responsibilities

• Build and enhance platform capabilities for model registries, feature registries, promotion policies, and governance tooling
• Design and deliver services for data preparation, annotation workflows, lineage, and auditability
• Develop cloud-native microservices and application programming interfaces to support scalable model delivery and operations
• Create large language model-powered capabilities (prompt design, agent workflows, retrieval) to deliver grounded, reliable outcomes
• Produce architecture and design artifacts and ensure implementations meet performance, resiliency, and security constraints
• Use company-approved AI-assisted development tools to improve quality and speed, validating outputs via reviews, testing, and secure coding practices
• Analyze telemetry and user feedback; build reporting and metrics to drive continuous improvement
• Identify hidden issues and patterns in systems and data; improve code health, observability, and platform architecture

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts
• Experience with modern architecture patterns (such as microservices, reactive architectures, event-driven architectures)
• Experience developing and deploying large language model-powered solutions using prompt design, agent workflows, tool integration, and retrieval-augmented generation
• Programming experience in at least two modern languages or frameworks (such as Python, Java, JavaScript, React, Node.js)
• Experience building and consuming RESTful application programming interfaces and tuning performance in large-scale applications
• Experience with cloud platforms and containerization or orchestration (such as Docker and Kubernetes)
• Experience with relational and non-relational databases (such as PostgreSQL, MongoDB, Redis, Elasticsearch, Cassandra)
• Experience with engineering practices including refactoring, design patterns, test-driven development, continuous integration and delivery, and application security
• Hands-on experience using company-approved AI-assisted software development tools, with the ability to validate and refine outputs for correctness, performance, and security

Preferred qualifications, capabilities, and skills

• Proficiency in Go or Terraform for automation, tooling development, or platform service implementation.
• Working knowledge of ML frameworks (PyTorch, TensorFlow, Hugging Face, scikit-learn) for model integration and operationalization.
• Exposure to multi-cloud or hybrid cloud architectures and platform portability strategies.