Senior AI Platform Engineer - AWS, Python & ML Systems

Aumni; Palo Alto (CA); Vor Ort; USD 180.000 - 230.000; Vollzeit

Zusammenfassung

JPMorganChase is seeking a Lead Software Engineer to design and deliver secure, scalable ML platforms. You will develop production-grade Python services, architect high-throughput distributed systems on AWS, and build cloud-native apps with EKS, ECS, Kafka, S3, and SQS.

You’ll drive API design, infrastructure-as-code, observability, and secure engineering practices while mentoring engineers. The role emphasizes collaboration with AI/ML teams, product managers, and SREs to achieve reliable,

Qualifikationen

• Formal training or certification on software engineering concepts and 5+ years applied experience.
• Advanced proficiency in Python programming, object-oriented design, and modular software architecture.
• Experience building and operating large-scale, high-performance cloud-native services within AWS environments.
• Hands-on experience with AWS technologies including EKS, ECS, MSK (Kafka), SQS, and S3.
• Strong experience implementing infrastructure-as-code solutions using Terraform and/or CloudFormation.
• Expertise in designing, deploying, and supporting distributed systems in production environments.
• Experience with observability, monitoring, logging, and alerting platforms such as Datadog, Dynatrace, and Splunk.
• Strong understanding of API design, microservices architecture, and scalable system design patterns.
• Experience implementing automated testing, CI/CD pipelines, deployment automation, and secure software engineering practices.
• Demonstrated experience utilizing approved AI-assisted software development tools for coding, code review, testing acceleration, troubleshooting, and operational support.
• Strong understanding of responsible AI usage, application security, resiliency requirements, compliance standards, and mentoring engineers on engineering best practices.

Aufgaben

• Design, develop, and maintain production-grade Python services and APIs that power high-impact machine learning platforms at enterprise scale
• Architect and implement high-throughput, low-latency distributed systems within AWS environments to support critical business workloads
• Build and manage scalable cloud-native applications leveraging AWS technologies including EKS, ECS, MSK, SQS, and S3
• Develop reusable service frameworks, shared libraries, and modular application components that accelerate engineering delivery across teams
• Design and implement infrastructure-as-code solutions using Terraform and CloudFormation to enable repeatable, auditable, and scalable deployments
• Create and maintain monitoring, alerting, and observability solutions utilizing platforms such as Datadog, Dynatrace, and Splunk to ensure operational excellence
• Deploy and support applications in production environments while ensuring adherence to service-level objectives and service-level agreements
• Implement secure-by-design engineering practices, automated testing, and deployment strategies including blue/green and canary releases
• Review code, provide architectural guidance, and mentor engineers on software engineering best practices to elevate team capability
• Collaborate with product managers, platform engineering teams, and site reliability engineers to deliver scalable, business-aligned solutions
• Drive adoption of enterprise-approved AI-assisted engineering practices to improve code quality, operational excellence, troubleshooting, and delivery efficiency

Kenntnisse

Python

AWS

Distributed systems

API design

CI/CD

Tools

Docker

Kubernetes

Terraform

CloudFormation

Datadog

S3

Jobbeschreibung

JPMorganChase is seeking a Lead Software Engineer to design and deliver secure, scalable ML platforms. You will develop production-grade Python services, architect high-throughput distributed systems on AWS, and build cloud-native apps with EKS, ECS, Kafka, S3, and SQS.

You’ll drive API design, infrastructure-as-code, observability, and secure engineering practices while mentoring engineers. The role emphasizes collaboration with AI/ML teams, product managers, and SREs to achieve reliable,