Senior Lead ML Inference Platform Engineer

Aumni; Seattle (WA); Presencial; USD 180.000 - 240.000; Jornada completa

Descripción de la vacante

JPMorgan Chase & Co. in Seattle seeks a Senior Lead Software Engineer to guide an agile team in building secure, scalable technology products. You’ll design and deploy cloud platforms optimized for AI/ML workloads, mentor engineers, and drive best practices across the SDLC, with a focus on reliability and security.

You will collaborate with AI teams, implement infrastructure-as-code, and lead efforts in cost optimization, performance, and code quality across complex systems.

Formación

• 5+ years of applied software engineering experience.
• Experience in system design, app development, testing and operational stability.
• Strong experience with Kubernetes and containerization (Docker).
• Proficiency in Python, Go, Java, or C#.
• Knowledge of cloud delivery models (IaaS, PaaS, SaaS) and deployment models.
• Understanding of ML concepts including transformers, training and inference.
• Experience with Infrastructure as Code.

Responsabilidades

• Provide technical guidance aligned to business goals with engineers, contractors, and vendors.
• Develop secure, high-quality production code and review others’ work.
• Influence design, functionality, and operations of applications.
• Champion SDLC tools, frameworks, and best practices.
• Architect and deploy secure, scalable cloud platforms for AI/ML workloads.
• Build CI/CD, automation, and IaC for ML deployment and ops.
• Monitor and optimize cloud resources for performance and cost.

Conocimientos

Kubernetes

Containerization

Python

Go

Java

C#

Cloud computing

Infrastructure as Code

AI/ML concepts

Code review

Educación

Software engineering certification

Herramientas

Docker

Prometheus

Grafana

MLflow

vLLM

Terraform

Descripción del empleo

JPMorgan Chase & Co. in Seattle seeks a Senior Lead Software Engineer to guide an agile team in building secure, scalable technology products. You’ll design and deploy cloud platforms optimized for AI/ML workloads, mentor engineers, and drive best practices across the SDLC, with a focus on reliability and security.

You will collaborate with AI teams, implement infrastructure-as-code, and lead efforts in cost optimization, performance, and code quality across complex systems.