Lead Software Engineer-AI Platform Engineer

Testing Site - JPMC; Seattle (WA); On-site; USD 180,000 - 240,000; Full time

Job summary

JPMorgan Chase in Seattle seeks a Lead Software Engineer within the Infrastructure Platforms team to deliver secure, scalable technology across business functions. You will act as a technical contributor shaping architecture, code, and deployment practices for high-quality products.

You will lead cloud infrastructure for AI/ML workloads, collaborate with AI teams, and drive automation and CI/CD across the software lifecycle in a fast-paced agile environment.

Qualifications

• Formal training or certification in software engineering concepts with 5+ years of applied experience.
• Hands-on practical experience in delivering system design, application development, testing, and ensuring operational stability.
• Proficiency in at least one programming language, such as Python, Go, Java, or C#.
• Proficiency in automation and continuous delivery methods.
• Proficient in all aspects of the Software Development Life Cycle.
• Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, AI, ML).
• Foundational understanding of machine learning concepts, including transformer architecture, ML training, and inference.
• Experience in solutions design and engineering, containerization (Docker, Kubernetes), and cloud service providers (AWS, Azure, GCP).
• Experience with Infrastructure as Code.
• Deep understanding of cloud component architecture: Microservices, Containers, IaaS, Storage, Security, and routing/switching technologies.

Responsibilities

• Design, develop, and troubleshoot software solutions with innovative approaches.
• Develop secure production code and review others' code.
• Automate remediation to improve stability of software.
• Lead architecture evaluation sessions with vendors and teams.
• Promote communities of practice and adoption of new technologies.
• Contribute to a culture of diversity, equity, inclusion and respect.
• Develop and deploy cloud infrastructure platforms for AI/ML workloads.
• Collaborate with AI teams to translate needs into infra requirements.
• Monitor, optimize cloud resources for performance and cost.
• Design CI/CD pipelines for ML workloads.
• Develop IaC scripts to streamline deployment tasks.

Skills

5+ years exp

Python

Go

Java

C#

Automation

CD/CI

SDLC

Machine Learning

Cloud

Docker

Kubernetes

AWS

Azure

GCP

Terraform

MLflow

Prometheus

Grafana

Education

Software Eng degree

Tools

Docker

Kubernetes

AWS

Azure

GCP

Terraform

MLflow

Prometheus

Grafana

Job description

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorgan Chase within the Corporate Sector, specifically as a part of the Infrastructure Platforms team, you will play a crucial role in an agile team committed to enhancing, creating, and delivering high-quality technology products in a secure, stable, and scalable manner. Your role as a vital technical contributor will involve developing critical technology solutions across numerous technical domains within various business functions, all aimed at supporting the firm's business goals.

Job Responsibilities:

• Execute creative software solutions, including design, development, and technical troubleshooting, with the ability to think beyond conventional approaches to build solutions or resolve technical problems.
• Develop secure, high-quality production code, and review and debug code written by others.
• Identify opportunities to eliminate or automate the remediation of recurring issues to enhance the overall operational stability of software applications and systems.
• Lead evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented assessments of architectural designs, technical credentials, and their applicability within existing systems and information architecture.
• Lead communities of practice across Software Engineering to promote awareness and adoption of new and leading-edge technologies.
• Contribute to a team culture of diversity, equity, inclusion, and respect.
• Develop and deploy cloud infrastructure platforms that are secure, scalable, and optimized for AI and machine learning workloads.
• Collaborate with AI teams to understand computational needs and translate these into infrastructure requirements.
• Monitor, manage, and optimize cloud resources to maximize performance and minimize costs.
• Design and implement continuous integration and delivery pipelines for machine learning workloads.
• Develop automation scripts and infrastructure as code to streamline deployment and management tasks.

Required Qualifications, Capabilities, and Skills:

• Formal training or certification in software engineering concepts with 5+ years of applied experience.
• Hands-on practical experience in delivering system design, application development, testing, and ensuring operational stability.
• Proficiency in at least one programming language, such as Python, Go, Java, or C#.
• Proficiency in automation and continuous delivery methods.
• Proficient in all aspects of the Software Development Life Cycle.
• Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.).
• Foundational understanding of machine learning concepts, including transformer architecture, ML training, and inference.
• Experience in solutions design and engineering, containerization (Docker, Kubernetes), and cloud service providers (AWS, Azure, GCP).
• Experience with Infrastructure as Code.
• Deep understanding of cloud component architecture: Microservices, Containers, IaaS, Storage, Security, and routing/switching technologies.

Preferred qualifications, capabilities, and skills

• Foundational understanding of NVIDIA GPU infrastructure software (e.g., DCGM, BCM, Triton Inference).
• Hands-on experience with machine learning frameworks such as PyTorch and TensorBoard.
• Proficiency with observability tools like Prometheus and Grafana.
• Experience in ML Ops and related tooling, including MLflow.
• Background in high performance computing and ML frameworks (e.g., vLLM, Ray.io, Slurm).
• Strong knowledge of network architecture, database programming (SQL/NoSQL), and data modeling.
• Familiarity with cloud data services, big data processing tools, and Linux environments (scripting and administration).