Lead Python & GenAI Engineer - ML Platforms

JPMorgan Chase Bank, N.A.; Plano (TX); On-site; USD 170,000 - 230,000; Full time

Job summary

JPMorganChase is seeking a Lead Software Engineer to drive architectural delivery of scalable, low-latency services in a secure, compliant environment. You will mentor teams, collaborate with stakeholders, and build ML pipelines for fraud detection and risk assessment while productionalizing models and advancing AI-enabled engineering practices.

The role requires strong Python/Java, Kubernetes, Linux, and microservices expertise, with focus on secure coding, auditability, and governance across

Qualifications

• Formal training or certification in software engineering with 5+ years of applied experience.
• Deep distributed-systems engineering in Python/Java plus modern microservices.
• Strong Python experience for AI/ML engineering, automation, and agentic AI services; REST APIs and microservices.
• Experience leading AI-assisted software development tools and validating AI outputs for correctness, performance, and security.
• Understanding of responsible AI use, data sensitivity, secure handling of inputs/outputs, resiliency and security.

Responsibilities

• Architect and deliver resilient, scalable services with low latency.
• Integrate services with enterprise systems, ensure security, governance, and auditability.
• Lead and mentor a development team; manage multiple deliverables across groups.
• Collaborate with stakeholders to design solutions and resolve issues.
• Build ML pipelines for fraud detection and risk assessment; productionalize models.
• Drives enterprise AI practices, code review, testing strategy, and traceability.
• Design and own reusable ML platform components with monitoring and alerting.

Skills

Python/Java

Distributed systems

REST APIs

AI-assisted tools

Linux

Kubernetes

Kafka/MQ

NoSQL Cassandra

CI/CD

Security/governance

Tools

Kafka/MQ

ELK

Splunk

Docker

Job description

JPMorganChase is seeking a Lead Software Engineer to drive architectural delivery of scalable, low-latency services in a secure, compliant environment. You will mentor teams, collaborate with stakeholders, and build ML pipelines for fraud detection and risk assessment while productionalizing models and advancing AI-enabled engineering practices.

The role requires strong Python/Java, Kubernetes, Linux, and microservices expertise, with focus on secure coding, auditability, and governance across